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Journal of Mechanical Design

The ASME Journal of Mechanical Design (JMD) serves the broad design community as a venue for scholarly, archival research in all aspects of the design activity.
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News & Updates

  • Exploring Effective Change Propagation in a Product Family Design

    Inayat UllahDunbing TangQi Wang and Leilei Yin

    J. Mech. Des 139(12), 121101; doi: 10.1115/1.4037627

    Delivering a variety of products with minimal lead time is a critical issue given today’s competitive manufacturing industry. Many design and production firms address the challenges of variety by adopting a product family manufacturing strategy. Product families are a broad range of artifacts, known as product variants, which share a number of common components. Thus, engineering changes in a product family affect the product under consideration and other product variants in the family. This increases the difficulty of predicting the change propagation within a family of products. This paper introduces a seven-step change propagation approach that predicts and evaluates the impact of change propagation across product variants. Interdependencies and logical relationships between directly connected components are captured using a Component-based Design Structure Matrix. This highlights the different change propagation paths that are available in the product’s structure. Risk analysis in terms of lead time is performed at the component level. The results demonstrate that avoiding project delays requires selecting suitable change propagation paths in a family of products.
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  • Effectiveness of an Immersive Virtual Environment for Collaboration With Gesture Support Using Low-Cost Hardware

    Joshua Q. Coburn, John L. Salmon and Ian Freeman
    J. Mech. Des 140(4), 042001 ; doi: 10.1115/1.4039006 

    Before the widespread use of modern computer systems, engineers worked in highly collaborative groups around large drawings tables. Today, the engineering design environment is more solitary, and collaboration often requires leaving the tools of the design environment. The highly distributed nature of today's workforce has caused a rapid proliferation of remote meetings and impeded the explanation of 3D information. While Virtual Reality (VR) has been proposed as a solution to these problems, the high cost and low availability of such systems has limited their impact. This paper presents a collaborative VR environment with support for hand gestures using readily-available, low-cost VR hardware. The environment allows multiple distributed participants to join a 3D virtual meeting, each with an independent view point, and walk around the virtual room to view the other participants as well as 3D engineering artifacts. The system supports natural communication gestures such as pointing, showing relative location, relative size, and orientation though physical hand motions. Additionally, participants can sketch in 3D using special input gestures. This allows for the communication of design concepts, design changes, and design issues. A user study is presented that demonstrates 45% faster communication compared against modern remote meeting software. Communication clarity and understanding are also improved. Future work will add deeper integration with modern engineering software and explore new design methods enabled by collaborative VR technology.  
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  • On Decentralized Optimization for a Class of Multi-Subsystem Co-Design Problems

    Tianchen Liu, Shapour Azarm and Nikhil Chopra
    J. Mech. Des 139(12); doi: 10.1115/1.4037893

    Co-design is the integrated optimization of the physical plant and controller for an engineering system. The challenge in co-design is determining both time-invariant (physical design) variables and time-variant (control) variables. In co-design, as the size of the problem (number of variables) becomes large, the problem can become too difficult for an all-at-once solution. Our approach extends earlier research by creating a class of multi-subsystem co-design problems where both design and control are formulated and solved. A scalable test problem is used for comparing the proposed decentralized co-design optimization approach against a centralized approach. Results of this study show that the computational time of the proposed decentralized approach increases approximately linearly with respect to an increase in the number of subsystems (variables), while the computational cost of the centralized approach increases nonlinearly.
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  • Additive Manufacturing-Enabled Part Count Reduction; A Lifecycle Perspective

    Sheng Yang and Yaoyao Fiona Zhao
    J. Mech Des 140(3):031702-031702-12. doi:10.1115/1.4038922

    Part count reduction (PCR) is one motivation for using additive manufacturing (AM) processes. PCR helps simplify product structure, eliminate auxiliary connecters, and reduce assembly difficulties and cost. However, PCR may also increase manufacturing difficulty and the irreplaceability of failed subcomponents. This paper presents a pioneering investigation of how AM-enabled PCR (AM-PCR) impacts lifecycle activities. A new set of design rules and principles are proposed for PCR that lead to lowered cost and enhanced performance. The PCR problem is formulated as a combinatory optimization problem where the objective is minimizing lifecycle cost/performance ratio while ensuring conformance to all constraints (e.g. manufacturing, maintenance, and recycling). To address the challenge of computational cost, a dual-level screening and refinement product redesign framework is presented that first searches for the minimum grouping solution and then refines the remaining combinations using design optimization. This approach will help designers automate the part count reduction process enabled by additive manufacturing while exploring new design innovation opportunities. 
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  • Editorial

    J. Mech. Des 140(1), 010201; doi: 10.1115/1.4038545 

    As I begin my term (January 2018 to December 2022), as Technical Editor for the Journal of Mechanical Design (JMD), I would like to wish a Happy New Year to all of the journal stakeholders: readers, authors, reviewers, Associate Editors, Guest Editors, and staff. I feel deeply honored and privileged to be appointed by the ASME Executive Committee of the Design Engineering Division (DED) and the Technical Committee on Publications and Communications (TCPC) to serve as the new Technical Editor of JMD. I feel especially honored to assume this role because I have always looked up to the past three Editors, Professors Michael McCarthy (University of California, Irvine, CA, 2003–2007), Panos Papalambros (University of Michigan, 2008–2012), and Shapour Azarm (University of Maryland, 2013–2017), as technical leaders and as role models in the field. Their dedication and leadership have led to the success of JMD, which is viewed as one of the few top journals world-wide in the area of design engineering. I am excited to follow their path, but also challenged to bring the journal to the next level of excellence.

    Journal of Mechanical Design serves the broad design community as the venue for scholarly, archival research in all aspects of the engineering design activity and welcomes contributions from all areas of design with an emphasis on synthesis. Example categories of topics include, but are not limited to: (1) design automation, (2) design theory and methodology, (3) design education, (4) design for manufacturing and the life cycle, (5) design of direct contact systems, including cams, gears, and power transmission, (6) design of mechanisms and robotic systems, (7) design of energy, fluid, and power handling systems, and (8) design innovation and devices. The connecting thread among these topics is the emphasis on design, rather than just analysis.

    During the past few years, JMD's impact factor has continuously improved, rising to 2.565 in year 2016, and is rated by ISI to be in the top quartile among 130 journals in the mechanical engineering field. The number of annual submissions has steadily increased to close to 870 papers in 2016. In my role as Editor, I will work toward ensuring an efficient, fair, and timely review process while maintaining the journal's high standards for paper quality. My predecessors have established an impressive array of best practices for journal operation, such as streamlining and promoting timely publication of contributions, inviting guest editorial and special issues for promoting emerging design areas, creating an editor's choice award for encouraging high quality work, and developing a new companion website1 as a valuable communication and promotional tool. I will continue these best practices while identifying and implementing new ideas for further advancing JMD.

    While it will certainly take me some time to learn about JMD's operations and develop new ideas, there are a few areas I plan to begin working on. First, I will strive to reduce further the review time from submission to publication by working closely with Associate Editors and journal staff. As a part of this effort, I will encourage and facilitate a faster conversion of the ASME conference papers to journal submissions. Second, I will work with international leaders in design engineering to further promote JMD world-wide, especially in regions where the submissions are currently low. Third, I will work on attracting technical leaders in the field to write review articles on key JMD topics. Fourth, to illustrate the relevance and impact of design research on industry practices, I will work to attract more submissions from industry, papers with industrial design applications, and papers on design innovation. Finally, to further bring up the level of scholarship in design research, I will promote the use of rigorous design research methods and raise the awareness of validation protocols.

    The past decade has seen a continued growth of interdisciplinary design research, beyond the traditional scope of mechanical design, that involves a wide range of engineering and nonengineering disciplines, e.g., materials science and engineering, mechanics, social science, arts and architecture, economics, market research, computer and information science, and communication studies, to name a few. Real design problems are not defined solely by technical concerns. They involve individuals, groups, organizations, and societies that call for cross-disciplinary collaborations and research. JMD will continue to embrace interdisciplinary design research topics and encourage submissions from teams of interdisciplinary researchers who work on theories and methods to support the design of emerging engineered systems.

    The success of JMD is based on the scholarly contributions of authors, dedicated reviewers, staff members supporting the journal, and our board of Associate Editors and Guest Editors who are leaders in their respective technical areas. The current Associate Editors include Oscar Altuzarra, Christina L. Bloebaum, Massimo Callegari, Dar-Zen Chen, Xiaoping Du, Scott Ferguson, James K. Guest, Katja Holtta-Otto, Harrison Kim, Nam H. Kim, Mian Li, Mohsen Kolivand, Gul E. Okudan Kremer, Yu-Tai Lee, Christopher Mattson, Samy Missoum, David Myszka, Ettore Pennestri, Carolyn Seepersad, Rikard Soderberg, Irem Tumer, G. Gary Wang, Paul Witherell, and Hai Xu. Guest Editors include Raymundo Arroyave, Andres Tovar, and Yan Wang. I thank all of the Associate and Guest Editors for their dedicated service to the journal. I am also pleased to let you know that Ms. Amy Suski, who has assisted the most recent Editor Shapour Azarm, is willing to continue on as Assistant to the Editor. During the past five years, JMD has benefited enormously from her experience in assisting the Editors of multiple journals.

    In summary, I am excited about this new opportunity to serve ASME and the broad technical community of engineering design. I look forward to working with every one of the JMD stakeholders to bring the journal to the next higher level of excellence.

    Copyright © 2018 by ASME
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  • STATE OF JOURNAL OF MECHANICAL DESIGN: A FIVE-YEAR REPORT (2013–2017)

     

    Shapour Azarm
    J. Mech. Des 139(12), 120201; doi: 10.1115/1.4038271 
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    Although it has been my privilege and honor to serve as Technical Editor (TE) of the ASME Journal of Mechanical Design (JMD) for the last five years, at the end of December 2017 when my term ends, I will hand over the journal to the excellent leadership of the next TE. As this is my last Editorial, it is an ideal opportunity to give a report on the state of the journal and express my gratitude to those who have contributed their hard work and expertise to JMD throughout my term (2013–2017).

    Over the last five years, JMD has received contributions from all areas of engineering design with emphasis on synthesis. Example topics include:


    (i) design automation, including design representation, virtual reality, geometric design, design evaluation, design optimization, risk- and reliability-based optimization, simulation-based design under uncertainty, design sensitivity analysis, system design integration, ergonomic and aesthetic considerations, design for market systems, data-driven design, origami and tessellation in design, design for user experience, needs and preferences, and design for materials and structures;

    (ii) 
    design of direct contact systems, including design of cams, gears, and power transmission systems;


    (iii) 
    design education;


    (iv) 
    design of energy, fluid, and power handling systems;


    (v) 
    design innovation and devices, including design of smart products and materials;


    (vi) 
    design for manufacturing and the life cycle, including design for the environment, DFX, design for additive manufacturing, and sustainable design;


    (vii) 
    design of mechanisms and robotic systems, including design of macro-, micro-, and nanoscaled mechanical systems, machine component, and machine system design; and


    (viii) 
    design theory and methodology, including creativity in design, decision analysis, design cognition, bio-inspired design, and design synthesis.


    Selected key statistics drawn from Journal Tool for the years 2013–2017 reveal a steady increase in the number of submissions, an increase in selectivity, and significant reduction in review times. For example:


    • In 2013, JMD had 567 submissions, with an acceptance rate (number of accepted papers divided by the number of (submitted − withdrawn1 − removed2 papers)) of about 32%.
    • In 2016, the number of submissions was 869, with an acceptance rate of about 25%.
    • The average time from submission to publication of an article was about 10.59 months in 2013, while in 2016 was reduced to 7.63 months. In 2013, the average review time, including two or more rounds of review, was 144 days. By 2016, the average review time had been reduced to 105 days.
    • As of this writing (October 2017), JMD has received 713 submissions out of which 121 are under review, 59 have been accepted (37 of which have been published), 312 rejected, 84 withdrawn, and 137 removed.
    The JMD's Journal Impact Factor (JIF)3 and 5-year JIF4 have also been improving for several years now. For JMD, JIF and 5-yr JIF have been as follows: for 2013, JIF = 1.165; 5-yr JIF = 1.394; for 2014, JIF = 1.250 and 5-yr JIF = 1.561; for 2015, JIF = 1.444 and 5-yr JIF = 1.688; and for 2016, JIF = 2.565 and 5-yr JIF = 3.017. According to ISI, JMD is rated to be in the top quartile among 130 journals in the mechanical engineering field.

    In addition to steadily improving its statistics, JMD has endeavored to solicit the best quality papers and promote the visibility of the journal. Since 2013, JMD has had a number of successful special issues on a variety of emerging topics, as listed below:

    • Special Issue on: “Origami and Tessellation in Design,” Guest Editors: Alexander H. Slocum, Larry L. Howell, and Mary I. Frecker, J. Mech. Des.135(11), 2013.
    • Special Issue on: “Biologically Inspired Design,” Guest Editors: Ashok K. Goel, Daniel A. McAdams, and Robert B. Stone, J. Mech. Des.136(11), 2014.
    • Special Issue on: “User Needs and Preferences in Engineering Design,” Guest Editors: Wei Chen, Carolyn C. Seepersad, and Bernard Yannou, J. Mech. Des.137(7), 2015.
    • Special Issue on: “Design for Additive Manufacturing,” Guest Editors: David W. Rosen, Carolyn C. Seepersad, Timothy W. Simpson, and Christopher B. Williams, J. Mech. Des.137(11), 2015.
    • Special Issue on: “Design Theory and Methodology,” Guest Editors: Matthew I. Campbell, Katja Hölttä-Otto, and Julie Linsey, J. Mech. Des.138(10), 2016.
    • Special Issue on: “Simulation-Based Design Under Uncertainty,” Guest Editors: Mian Li, Sankaran Mahadevan, Samy Missoum, and Zissimos P. Mourelatos, J. Mech. Des.138(11), 2016.
    • Special Section on: “Designing for Additive Manufacturing,” Guest Editors: Jesse Boyer, Carolyn Conner Seepersad, Timothy W. Simpson, Christopher B. Williams, and Paul Witherell, J. Mech. Des.139(10), 2017.
    • Special Section on: “Data-Driven Design (D3),” Guest Editors: Harrison Hyung Min Kim, Ying Liu, Charlie C. L. Wang, and Yan Wang, J. Mech. Des.139(11), 2017.

    Following the example of some of the other ASME journals, JMD also began the process of recognizing outstanding papers. In this regard, in May 2014 I wrote an Editorial in JMD titled: “Announcing JMD's Annual Best Paper Award Guidelines.” That editorial discussed the motivation, purpose, criteria for selection, and selection process. However, after further thoughts and inputs from other ASME Editors, it was decided to rename the award as the “Editors' Choice” paper award rather than “Best Paper” award. I then formed and charged a committee to select Editors' Choice papers from a list of papers that were nominated and voted by associate and special issue guest editors. From that list, the committee selected one paper for each of the years 2014–2016 and granted Editor's Choice Paper Award to the authors of each. The papers awarded were as follows:

    • “A Descriptor-Based Design Methodology for Developing Heterogeneous Microstructural Materials System,” Hongyi Xu, Yang Li, Catherine Brinson, and Wei Chen, J. Mech. Des.136(5), 2014.
    • “Level Set Topology Optimization of Printed Active Composites,” Kurte Maute, Anton Tkachuk, Jiangtao Wu, H. Jerry Qi, Zhen Ding, and Martin L. Dunn, J. Mech. Des.137(11), 2015.
    • “A Stiffness Formulation for Spline Joints,” J. Hong, D. Talbot, and A. Kahraman, J. Mech. Des.138(4), 2016.
    In an effort to promote the visibility of the journal, through the work of Associate Editor Harrison Kim and Editorial Assistant Amy Suski, JMD launched a new version of the companion website,5 including a “Featured Articles” section that highlights the research contributions of work published by the journal in an abridged format that can be widely shared in industry, academia, and social media outlets.

    The continued success of JMD has been due to the extraordinary hard work and dedication of numerous individuals, including technical editors (and their editorial board) who served the journal before me, associate editors, guest editors, my Editorial Assistant (Amy Suski) whom I cannot thank enough for doing her job with utmost professionalism and precision, and the ASME publication staff (Colin McAteer, Journals Manager; Jennifer Smith, Production Coordinator; and ASME staff Beth Darchi and Tamiko Fung) who patiently resolved numerous publication related issues. I am also indebted to our reviewers whose insightful reviews clearly show that they do care deeply about the quality of papers published in the journal.

    Finally, I wish all the best for the next TE of JMD!

    Below is a listing and short biography of the associate editors and special issue guest editors who served the journal during the period 2013–2017:

    Associate Editors:

    Janet K. Allen, Associate Editor 2006–2013, earned her S.B. degree from the Massachusetts Institute of Technology and Ph.D. from the University of California, Berkeley, CA. She is a Professor and John and Mary Moore Chair of Industrial Engineering at the University of Oklahoma. The focus of Dr. Allen's research is a simulation-based design of complex systems and the management of uncertainty.

    Oscar Altuzarra, Associate Editor 2012 to present, received his M.Sc. Mechanical Engineering degree and a Ph.D. degree in Mechanical Engineering from the Engineering School of Bilbao, Universidad del País Vasco (UPV/EHU), Leioa, Spain, and a Diploma in higher studies from the Coventry University in Coventry, Coventry, UK. He is a Professor in the Department of Mechanical Engineering at the Engineering School of Bilbao, UPV/EHU. His research interests are theoretical kinematics, mechanisms, design of parallel kinematic machines, robotics, and computational solutions to complex mechanical problems in the field of the theory of mechanisms.

    Shorya Awtar, Associate Editor 2013–2015, earned a B.Tech. from the Indian Institute of Technology Kanpur, M.S. from the Rensselaer Polytechnic Institute, and Sc.D. from the Massachusetts Institute of Technology. He is an Associate Professor at the University of Michigan and the Founder and Chief Technology Officer of FlexDex Surgical. His research interests include machine design, flexure mechanisms, parallel kinematics, mechatronic systems, and precision engineering. Application areas include medical devices for minimally invasive surgery, motion stages for metrology and manufacturing, electromagnetic and electrostatic actuators, and microsystems.

    Christina L. Bloebaum, Associate Editor 2016 to present, received her B.S., M.S., and Ph.D. degrees in aerospace engineering from the University of Florida in Gainesville, FL. She is the Dennis and Rebecca Muilenburg Professor of Aerospace Engineering at Iowa State University (ISU) in Ames, IA. She is also a member of the Virtual Reality Applications Center (VRAC) and the Human–Computer Interaction program at ISU. She conducts research in design of complex engineered systems, with an emphasis on achieving consistency in physics through incorporation of multidisciplinary design optimization as well as preferences through incorporation of value-based systems engineering and decision analysis.

    Diann Brei, Associate Editor 2008–2013, earned her B.S.E degree in Computer Systems Engineering and her Ph.D. in Mechanical Engineering from Arizona State University. She is a Professor of Mechanical Engineering Department at the University of Michigan, Ann Arbor, MI and co-directs the General Motors/University of Michigan Multifunctional Vehicle Systems Collaborative Research Laboratory. Her research interests include integrated design methodology/processes, device innovation, smart materials and structures, and actuation.

    Jonathan Cagan, Associate Editor 1998–2001 and 2008–2014, received his Bachelor of Science and Master of Science from the University of Rochester, and his Ph.D. from the University of California at Berkeley, all in Mechanical Engineering. He is the George Tallman and Florence Barrett Ladd Professor in Engineering, in the Department of Mechanical Engineering at Carnegie Mellon University, with courtesy appointment in the School of Design. At Carnegie Mellon he serves as Associate Dean for Graduate and Faculty Affairs in the College of Engineering, co-directs the Integrated Innovation Institute, and is a faculty co-director of the Swartz Center for Entrepreneurship. His research focuses on product development, computational innovation, and cognitive-based engineering.

    Massimo Callegari, Associate Editor 2015 to present, received the Laurea degree in Mechanical Engineering from the University of Genova, Genova, Italy. He is a Professor of Machine Mechanics, Chair of the Board of Teachers of Mechanical Engineering degrees, member of the Steering Committee, and Deputy Director of the department of Industrial Engineering and Mathematical Sciences at the Faculty of Engineering of the Polytechnic University of Marche in Ancona, Italy. He has participated into different national and international research projects in the fields of automation, robotics, and innovative handling devices.

    Dar-Zen Chen, Associate Editor 2015-present, received his B.S. degree from National Taiwan University (NTU) and M.S. and Ph.D. degrees in Mechanical Engineering from the University of Maryland, College Park, MD. He is a professor in the Department of Mechanical Engineering and Institute of Industrial Engineering at National Taiwan University. In addition to robotics, kinematics, and mechanism design, his research interests also cover intellectual property management, scientometrics, and competitive analysis.

    Wei Chen, Associate Editor 2003–2006 and 2010–2013 and Guest Editor 2014–2015, earned her Ph.D. from the Georgia Institute of Technology, M.S. from University of Houston, and B.S. from Shanghai Jiao Tong University, China, all in mechanical engineering. She is a Wilson-Cook Chair Professor in Engineering Design at Northwestern University in the Department of Mechanical Engineering. Her research focuses on design under uncertainty, consumer choice modeling, and decision making in design.

    Olivier L. de Weck, Associate Editor 2010–2013, obtained his degree in Industrial Engineering from ETH Zurich and S.M. and Ph.D. degrees in Aerospace Engineering from the Massachusetts Institute of Technology (MIT). He is an Associate Professor of Engineering Systems and Aeronautics and Astronautics at MIT. His research focuses on understanding how complex man-made systems evolve over time and how we can design them to be more changeable while maximizing lifecycle value.

    Andy Dong, Associate Editor 2013–2016, earned a B.S., M.S., and Ph.D. from the University of California at Berkeley, all in Mechanical Engineering. He is a Professor and holds the Warren Centre Chair for Engineering Innovation in the Faculty of Engineering and Information Technologies at the University of Sydney. Professor Dong is an expert in the analysis of design data such as organizational interactions, design documents, and product data to forecast and manage the performance of engineering design.

    Xiaoping Du, Associate Editor 2016 to present, received his Ph.D., M.S., and B.S. degrees in Mechanical Engineering from the University of Illinois at Chicago, IL, Chongqing University, Chongqing, China, and Shanghai Jiaotong University, Shanghai, China, respectively. He is a Curator's Distinguished Teaching Professor in the Department of Mechanical and Aerospace Engineering at Missouri University of Science and Technology. His research focuses on design under uncertainty, reliability, and optimization.

    Qi Fan, Associate Editor 2012 to present, received his M.S. degree in mechanical engineering at Wuhan Transportation University and his Ph.D. from the University of Illinois at Chicago. He is a Senior Gear Theoretician and Director of Bevel Gear Technology (China) at The Gleason Works. He is the current Chair of the Committee of ASME Power Transmission and Gearing. His areas of interest include gear geometry and application, gear manufacturing process, machine tools, and machine elements.

    Scott Ferguson, Associate Editor 2016 to present, received his B.S., M.S., and Ph.D. in Mechanical Engineering from the University at Buffalo. He is an Associate Professor in the Department of Mechanical and Aerospace Engineering at North Carolina State University and the director of the System Design Optimization Lab. His research in engineering design and system optimization explores challenges associated with the design of complex engineered systems and market-driven product design.

    Zhang-Hua Fong, Associate Editor 2013–2015, received a B.S. degree from National Chung Hsing University and M.S. and Ph.D. degrees from National Chiao Tung University, all in mechanical engineering. He is a Research Professor in the Department of Mechanical Engineering and Dean of the College of Engineering at National Chung Cheng University in Taiwan.

    Mary Frecker, Associate Editor 2005–2011 and Guest Editor 2012–2013, has a B.S. from the University of Dayton and an M.S. and Ph.D. in Mechanical Engineering from the University of Michigan. She is a Professor of Mechanical and Biomedical Engineering at the Pennsylvania State University. Her areas of interest include optimal design, compliant mechanisms, smart structures, and medical device design.

    Feng Gao, Associate Editor 2012–2014, received his Ph.D. in mechanical engineering from the Beijing University of Aeronautics and Astronautics, China. He is a full professor and serves as the director of State Key Lab of Mechanical Systems and Vibration at the Shanghai Jiao Tong University. His research areas include macro- and microparallel manipulators, humanoid and multileg robots, and design and control of heavy-duty machinery with parallel mechanisms.

    Ashitava Ghosal, Associate Editor 2006–2013, obtained B.Tech., M.S. and Ph.D. degrees in mechanical engineering from the Indian Institute of Technology at Kanpur, University of Florida at Gainesville and Stanford University, respectively. He is a Professor of Mechanical Engineering and a faculty member of the Centre for Product Design and Manufacturing at the Indian Institute of Science, Bangalore. His research interests include robotics and multibody mechanical systems, design of mechanical systems, and product design.

    Massimiliano Gobbi, Associate Editor 2014–2017, was awarded a master's degree in Mechanical Engineering from Politecnico di Milano, Milan, Italy, and Ph.D. in Applied Mechanics. He is an Associate Professor of Mechanical Engineering at Politecnico di Milano. His research focuses on road vehicles engineering, optimization of complex systems, and advanced design.

    David J. Gorsich, Associate Editor 2009–2015, received a Ph.D. in applied mathematics from the Massachusetts Institute of Technology, an M.S. in applied mathematics from George Washington University, and a B.S. in electrical engineering from Lawrence Technological University. He is the Chief Scientist of the U.S. Army Tank Automotive Research, Development, and Engineering Center (TARDEC). His areas of expertise include simulation, reliability-based design optimization, terrain modeling, spatial statistics, and other approximation methods.

    James Guest, Associate Editor 2014 to present, received his Ph.D. and M.S.E. from Princeton University, and B.S.E. from the University of Pennsylvania, all in Civil Engineering. He is an Associate Professor of Civil Engineering at the Johns Hopkins University (JHU) and leads the JHU Topology Optimization Group whose research focuses on developing topology optimization algorithms for the design of materials and structures.

    Katja Hölttä-Otto, Guest Editor 2015–2016 and Associate Editor 2016 to present, received her M.Sc. and Ph.D. in Mechanical Engineering from Helsinki University of Technology. She is an Associate Professor of product development at the Design Factory at Aalto University, Espoo, Finland. Her areas of specialization include creativity, need finding, design methodologies, and modular product platforms.

    Larry L. Howell, Associate Editor 2004–2008 and Guest Editor 2012–2013, received his B.S. degree from Brigham Young University and his M.S. and Ph.D. degrees from Purdue University. He is a Professor, Associate Dean, and past chair of the Department of Mechanical Engineering at Brigham Young University (BYU), where he holds a University Professorship. Professor Howell's patents and technical publications focus on compliant mechanisms.

    Chintien Huang, Associate Editor 2012–2014, received his B.S. degree from National Chung Hsing University and M.S. and Ph.D. degrees from Stanford University, all in mechanical engineering. He is a Professor in the Department of Mechanical Engineering at National Cheng Kung University in Taiwan. His areas of expertise include theoretical/computational kinematics and mechanism design.

    Charles Kim, Associate Editor 2015–2016, received his B.S. in Mechanical Engineering from the California Institute of Technology and M.S.E. and Ph.D. in Mechanical Engineering from the University of Michigan, Ann Arbor, MI. He is an Associate Professor of Mechanical Engineering at Bucknell University. Professor Kim's primary technical research interests are in methodologies for the design of compliant systems and soft robotic actuators. Professor Kim is also involved in numerous curricular and co-curricular initiatives to synthesize design, innovation, and entrepreneurship.

    Harrison Kim, Associate Editor 2013 to present and Guest Editor 2016–2017, received his B.S. and M.S. from the Korea Advanced Institute of Science and Technology, and his Ph.D. from the University of Michigan, all in Mechanical Engineering. He is a Professor in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign and is affiliated with the Computational Science and Engineering Program at the University of Illinois. His research focuses on complex systems design, product design analytics, multidisciplinary design optimization, sustainability, market systems, and energy systems engineering.

    Nam-Ho Kim, Associate Editor 2015-present, received his Ph.D. from the Department of Mechanical Engineering at the University of Iowa. He is a Professor of Mechanical and Aerospace Engineering at the University of Florida. His research areas are structural design optimization, design sensitivity analysis, design under uncertainty, prognostics and health management, nonlinear structural mechanics, and structural-acoustics.

    Michael Kokkolaras, Associate Editor 2008–2014, received a Diploma in Aerospace Engineering from the Technical University of Munich and a Ph.D. in Mechanical Engineering from Rice University. He is an Associate Professor of Mechanical Engineering at McGill University. His research interests include multidisciplinary optimization, simulation-based engineering design, uncertainty quantification, decomposition and coordination methods, modeling and validation, systems of systems, product families, and optimization applications in engineering.

    Mohsen Kolivand, Guest Editor 2016–2017 and Associate Editor 2017 to present, received his B.S. and M.S. from Tehran University and his Ph.D. from The Ohio State University, all in Mechanical Engineering. Dr. Kolivand is a Bevel Gear Design Manager at American Axle and Manufacturing, Inc., Detroit, MI. His areas of interest include gear geometry, gear manufacturing and inspection, gear efficiency, life estimation, wear analysis, and noise, vibration and harshness evaluation.

    Gül E. Kremer, Associate Editor 2014 to present, received her Ph.D. from the Department of Engineering Management and Systems Engineering of Missouri University of Science and Technology. She is a Professor and C.G. “Turk” & Joyce A. Therkildsen Department Chair of Department of Industrial and Manufacturing Systems Engineering at The Iowa State University. Professor Kremer's research interests are in the areas of design education, design decision-making, and sustainability in product design.

    Yu-Tai Lee, Associate Editor 2015 to present, received a B.S. in Mechanical Engineering from the National Taiwan University, and his M.S. and Ph.D. in Mechanics and Hydraulics from the University of Iowa. He was a Senior Scientist at the Computational Hydromechanics Division of Naval Surface Warfare Center, Carderock Division. His work has included designing high-pressure fans for Navy's mission-critical shipboard ventilation systems, and coupling computational fluid dynamics optimization schemes for naval ship HVAC compressors and hovercraft lift fans.

    Mian Li, Guest Editor 2015–2016 and Associate Editor 2017 to present, earned his B.E. and M.S. in Control Engineering from Tsinghua University China, and his Ph.D. from the Department of Mechanical Engineering, University of Maryland at College Park. He is an Associate Professor in the University of Michigan-Shanghai Jiao Tong University Joint Institute and adjunct Associate Professor at the School of Mechanical Engineering, at Shanghai Jiao Tong University. His research work has been focused on robust/reliability-based multidisciplinary design optimization and control.

    Craig Lusk, Associate Editor 2012–2015, earned his M.S. from Virginia Tech and Ph.D. from Brigham Young University. He is an Associate Professor in the Mechanical Engineering Department at the University of South Florida, where he teaches graduate and undergraduate courses on mechanisms and applied elasticity. His research interests include compliant mechanisms, MEMS, biomechanics, and spherical/spatial mechanisms.

    Christopher A. Mattson, Associate Editor 2013 to present, received his B.S. and M.S. degrees from Brigham and Young University and his Ph.D. from the Department of Mechanical and Aerospace Engineering at Rensselaer Polytechnic Institute. He is a Professor of Mechanical Engineering at Brigham Young University (BYU). Professor Mattson's research interests include product development, multi-objective optimization, computational design, and design for the developing world.

    Samy Missoum, Guest Editor 2015–2016 and Associate Editor 2016 to present, received his doctorate in Mechanical Engineering from the National Institute of Applied Sciences in Toulouse, France. He is an Associate Professor in the Aerospace and Mechanical Engineering Department at the University of Arizona and the Director of the Computational Design Optimization of Engineering Systems (CODES) Laboratory. His research focuses on the development and advanced applications of new optimization, reliability, and risk assessment techniques for nonlinear problems exhibiting a high sensitivity to uncertainty.

    Zissimos P. Mourelatos, Associate Editor 2009–2013 and Guest Editor 2015–2016, earned his Ph.D. from the University of Michigan. He is a Professor of Mechanical Engineering at Oakland University and holds the John F. Dodge Chair position of Engineering. Professor Mourelatos conducts research in the areas of design under uncertainty, structural reliability methods, reliability analysis with insufficient data, reliability-based design optimization, vibrations and dynamics, and noise, vibration, and harshness.

    David H. Myszka, Associate Editor 2015 to present, received B.S. and M.S. degrees in mechanical engineering from the State University of New York at Buffalo, and M.B.A. and Ph.D. in mechanical engineering from the University of Dayton. He is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of Dayton and co-director of the Design of Innovative Machines Laboratory, where he is involved in several academic and industrial projects related to machine and mechanism design, analysis, and experimentation.

    Shinji Nishiwaki, Associate Editor 2012–2015, received his B.E. and M.E. degrees in the Department of Precision Engineering from Kyoto University, and Ph.D. in the Department of Mechanical Engineering and Applied Mechanics from the University of Michigan. He is a Professor in the Department of Mechanical Engineering and Science at Kyoto University, Japan. His areas of interest include topology optimization, optimum system design, and multidisciplinary design optimization.

    Christiaan J. J. Paredis, Associate Editor 2011–2013, has an M.S. degree in Mechanical Engineering from the Catholic University of Leuven (Belgium), and an M.S. and Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University. He is a Professor and Woodruff Faculty Fellow in the G.W. Woodruff School of Mechanical Engineering at Georgia Tech. His areas of interest include Model-Based Systems Engineering, decision-making under uncertainty, and design optimization.

    Matthew Parkinson, Associate Editor 2012–2015, holds a Ph.D. in Biomedical Engineering, an M.S. in Industrial and Operations Engineering from the University of Michigan, and an M.S. in Mechanical Engineering from Brigham Young University. He is a Professor and Director of the Learning Factory at Pennsylvania State University in the College of Engineering. His research efforts focus on tools and methodologies for the design of artifacts that are robust to human variability.

    Ettore Pennestrì, Associate Editor 2013 to present, received Laurea in Mechanical Engineering from the University of Rome La Sapienza and an M.S. and Doctor of Engineering Science from Columbia University, New York. He is a Professor of Mechanics Applied to Machines at the University of Roma Tor Vergata, Italy, and holds a teaching appointment at Università Campus Biomedico in Rome. His areas of specialization include powertrain design, mechanisms design, computational kinematics, biomechanics, and multibody dynamics.

    Karthik Ramani, Associate Editor 2008-2014, earned his B.Tech. from the Indian Institute of Technology, Madras, M.S. from the Ohio State University, and Ph.D. from Stanford University, all in Mechanical Engineering. He is a Professor in the School of Mechanical Engineering and of Electrical and Computer Engineering (by Courtesy) at Purdue University. His expertise includes digital and computational geometry, shape design and analysis, shape and ontology search, and computational tools for early design innovation.

    Kazuhiro Saitou, Associate Editor 2013–2016, received a B.Eng. degree from the University of Tokyo and M.S. and Ph.D. degrees from the Massachusetts Institute of Technology. He is a Professor of Mechanical Engineering at the University of Michigan, Ann Arbor, MI. His research interests include assembly design, structural optimization, manufacturing systems, and biomedical image processing.

    James P. Schmiedeler, Associate Editor 2007–2013, received a B.S. degree from the University of Notre Dame and M.S. and Ph.D. degrees from The Ohio State University, all in mechanical engineering. He is a Professor in the Department of Aerospace and Mechanical Engineering at the University of Notre Dame. Professor Schmeideler's areas of interest include machine design, robotics, and biomechanics.

    Carolyn Conner Seepersad, Associate Editor 2013 to present and Guest Editor 2014–2017, earned her B.S. degree from West Virginia University and M.S. and Ph.D. from Georgia Tech, all in mechanical engineering. She also earned a B.A./M.A. from Oxford University as a Rhodes Scholar. She is an Associate Professor and General Dynamics Faculty Fellow in the Mechanical Engineering Department at the University of Texas at Austin. Her research focuses on design automation, design of engineering materials and structures, set-based design, design for additive manufacturing, conceptual design, and innovation.

    Kristina Shea, Associate Editor 2013–2016, earned her B.S., M.S., and Ph.D. in mechanical engineering from Carnegie Mellon University. She is a Professor for Engineering Design and Computing at ETH Zürich in Switzerland. Professor Shea's areas of expertise include design methods, design representations, synthesis, computational design, model-based design, and additive manufacturing.

    Timothy W. Simpson, Associate Editor 2006–2013 and Guest Editor 2014–2017, received a B.S. degree in mechanical engineering from Cornell University and M.S. and Ph.D. degrees in mechanical engineering from the Georgia Institute of Technology. He is the Paul Morrow Professor of Engineering Design and Manufacturing at the Pennsylvania State University in University Park. He also holds faculty appointments in Mechanical and Nuclear Engineering, Industrial and Manufacturing Engineering, Architecture, the School of Engineering Design, Technology, and Professional Programs, and the College of Information Sciences and Technology. His research focuses on product family design, product platforms, additive manufacturing, and design innovation.

    Avinash Singh, Associate Editor 2007–2013, received his B. Tech. degree from the Institute of Technology, BHU, India, and his M.S. and Ph.D. degrees in Mechanical Engineering from the Ohio State University. Dr. Singh is an Engineering Group Manager—Advanced Torque Converters and Gear Systems, in the Advanced Power Transfer Group of GM Powertrain, General Motors Corporation. He works on power transmission component technology and his research interests are in the areas of gear system design and analysis, dynamics and noise, development and validation of high fidelity models, power losses, rotating system diagnostics, and fatigue life prediction.

    Alexander H. Slocum, Associate Editor 2009–2014 and Guest Editor 2012–2013, earned S.B., S.M., and Ph.D. degrees from the Massachusetts Institute of Technology (M.I.T.). He is the Walter M. May and Hazel May Professor of Mechanical Engineering at the Massachusetts Institute of Technology. His areas of interest include machine elements, precision machine design, medical devices, and energy harvesting machines.

    Rikard Söderberg, Associate Editor 2012 to present, received his Ph.D. from Chalmers University of Technology. He is the head of the department for Industrial and Materials Science and Director for Wingquist Laboratory. Dr. Söderberg has been a scientific advisor for the Fraunhofer Chalmers Centre of Industrial Mathematics since it was found in 2001 and is Chairman of its Board of Directors. His research focuses on minimizing the effect of geometrical variation and includes industrial design aspects, visualization, robust design, statistical variation simulation, optimization, assembly modeling and analysis, inspection preparation, and analysis.

    Janis Terpenny, Associate Editor 2008–2014, earned a B.S. degree in Applied Mathematics from Virginia Commonwealth University, an M.S. degree in Industrial Engineering and Operations Research, and a Ph.D. degree in Industrial and Systems Engineering from Virginia Tech. She is the Peter & Angela Dal Pezzo Chair and Department Head of the Harold & Inge Marcus Department of Industrial and Manufacturing Engineering at Penn State. Her research interests include engineering design and smart manufacturing, knowledge and information in design, product families and platforms, product obsolescence, complexity of products and systems, cloud computing, and design education.

    Kwun-Lon Ting, Associate Editor 2006–2014, received a B.S. from National Taiwan University, M.S. from Clemson University, and Ph.D. from Oklahoma State University. He is a Professor of Mechanical Engineering at Tennessee Tech. His research interests include kinematics, compliant mechanisms, robotics, and optimization.

    Irem Tumer, Associate Editor 2012 to present, received her Ph.D. in Mechanical Engineering from The University of Texas at Austin. She is a Professor at Oregon State University, where she leads research in complex system design as part of the Design Engineering Labs, and currently serves as Associate Dean for Research for the College of Engineering. Her expertise is system-level design and analysis for software-intensive engineered systems, focusing on risk and failure analysis and engineering design theory and methodology.

    G. Gary Wang, Associate Editor 2013 to present, received his B.Sc. and M.Sc. from School of Mechanical Engineering, Huazhong University of Science and Technology, and obtained his Ph.D. in mechanical engineering from University of Victoria. He is a professor at Simon Fraser University (SFU) in Vancouver, BC, Canada. His research focuses on engineering optimization, metamodel-based design optimization, design visualization, and design for manufacturing.

    Paul Witherell, Guest Editor 2016–2017 and Associate Editor 2017 to present, received his Ph.D. from the Department of Mechanical and Industrial Engineering at the University of Massachusetts Amherst. He is a Mechanical Engineer in the Systems Integration Division of the Engineering Laboratory at the National Institute of Standards and Technology, where he manages a project on Systems Integration for Additive Manufacturing and serves as the Associate Program Manager of the Measurement Science for Additive Manufacturing program in the Engineering Laboratory. His research interests include design for additive manufacturing, digital thread for additive manufacturing, design optimization, knowledge representation in product development, ontology and semantic relatedness for design manufacturing, and sustainable manufacturing.

    Hai Xu, Associate Editor 2015 to present, received his Ph.D., M.S., and B.S. degrees in Mechanical Engineering from The Ohio State University, The University of Michigan-Dearborn, and Nanjing University of Science and Technology, China, respectively. He is a Senior Staff Engineer of the General Motors Company and serves as a Driveline Gear Technical Specialist at GM's Global Vehicle Components and Subsystems Unit, primarily responsible for hypoid gearing technology development. His expertise is in gear design and manufacturing methods, gear geometry and applications, gear tribology, power loss, fatigue, and noise and vibration.

    Hong-Sen Yan, Associate Editor 2007–2013, holds a B.S. from the National Cheng Kung University (NCKU), M.S. from the University of Kentucky, and Ph.D. from Purdue University, all in mechanical engineering. He is an NCKU Chair Professor and an honorary member of IFToMM. Professor Yan's areas of interest include kinematics, conceptual design of mechanisms and machines, and reconstruction design of ancient machinery.

    Bernard Yannou, Associate Editor 2008–2015 and Guest Editor 2014–2015, received his M.S. in Mechanical Engineering from Ecole Normale Supérieure of Cachan (ENSC), M.S. in Computer Science from Paris-6 University, and Ph.D. in Industrial Engineering from ENSC. He is a Professor of Industrial and Design Engineering and head of the Industrial Engineering Research Department, CentraleSupélec. His areas of interest include design science, design automation, design management/methodologies/new product development, artificial intelligence in design, innovation engineering, and sustainable design.


    Special Issue Guest Editors:


    Jesse R. Boyer, Guest Editor 2016–2017, holds two B.S.E. degrees from the University of Michigan in Aerospace Engineering and Naval Architecture and Marine Engineering. He is currently the Additive Manufacturing Fellow at Pratt & Whitney (P&W) and is involved curriculum development at the University of Connecticut and the University of Hartford. His research focus is on key process variables to control additive manufacturing, in-process monitoring for production, digital thread related to inspection, and additive manufacturing.

    Matt Campbell, Guest Editor 2015–2016, received his B.S., M.S., and Ph.D. from Carnegie Mellon University. Dr. Campbell is a Professor of Mechanical Engineering at Oregon State University with research focusing on methods that independently create solutions for typical mechanical engineering design problems like gear trains, sheet metal, planar mechanisms, and planning for manufacturing, assembly, and disassembly. He has expertise in a variety of fields such as machine design, design theory, artificial intelligence, graph theory, and numerical optimization.

    Clive L. Dym, Guest Editor 2015–2016, completed the B.S.C.E. at Cooper Union, an M.S. at Brooklyn Polytechnic Institute, and Ph.D. at Stanford University. Dr. Dym was a Professor Emeritus of Engineering at Harvey Mudd College where he was the Fletcher Jones Professor of Engineering Design and Director of the Center for Design Education at Harvey Mudd, as well as Engineering Department Chair. His interests included design theory, knowledge-based (expert) systems for engineering design, and structural and applied mechanics.

    Ashok K. Goel, Guest Editor 2013–2015, earned his Ph.D. from the Ohio State University. He is a Professor in the School of Interactive Computing, and an Adjunct Professor in the School of Computational Science and Engineering and the Woodruff School of Mechanical Engineering at Georgia Institute of Technology in Atlanta. He is also the Director of Interactive Computing's Design and Intelligence Laboratory and a Co-Director of Georgia Tech's Center for Biologically Inspired Design. Professor Goel conducts research into human-centered computing, artificial intelligence, and cognitive science, with a focus on computational design, modeling, and creativity.

    Julie S. Linsey, Guest Editor 2015–2016, received her Ph.D. in Mechanical Engineering at the University of Texas at Austin. She is an Associate Professor in the George W. Woodruff School of Mechanical Engineering at the Georgia Institute of Technology. She founded and leads the Innovation, Design Reasoning, and Engineering Education (IDREEM) Lab. Dr. Linsey's research area is design cognition including systematic methods and tools for innovative design with a particular focus on concept generation and design-by-analogy.

    Ying Liu, Guest Editor 2016–2017, obtained his Bachelor and Master's degrees from the Mechanical Engineering Department at the Chongqing University, China, and his Ph.D. from the Innovation in Manufacturing Systems and Technology program under the Singapore MIT Alliance at the National University of Singapore. He is an Associate Professor with the Institute of Mechanical and Manufacturing Engineering at the School of Engineering in Cardiff University, Cardiff, Wales, UK. His research interests focus primarily on design informatics, manufacturing informatics, intelligent (digital) manufacturing, design methodology and process, product design, and advanced ICT in design and manufacturing.

    Sankaran Mahadevan, Guest Editor 2015–2016, obtained his B.Tech. from the Indian Institute of Technology, Kanpur, M.S. from Rensselaer Polytechnic Institute, and Ph.D. from Georgia Institute of Technology. Professor Mahadevan is the John R. Murray Sr. Professor of Engineering at Vanderbilt University, where he has appointments in Civil and Environmental Engineering and Mechanical Engineering and is a Co-Director of Laboratory for Systems Integrity and Reliability. His areas of research interest include reliability and risk analysis, design optimization, structural health monitoring, model verification and validation, and uncertainty quantification.

    Daniel A. McAdams, Guest Editor 2013–2015, received his Ph.D. from the University of Texas at Austin. He is a Professor of Mechanical Engineering in the Department of Mechanical Engineering at Texas A&M University and directs the Product Synthesis Engineering Research Lab. Dr. McAdams' research interests are in the area of design theory and methodology with specific focus on functional modeling, innovation in concept synthesis, biologically inspired design methods, inclusive design, and technology evolution as applied to product design.

    David Rosen, Guest Editor 2014–2015, received his Ph.D. at the University of Massachusetts in mechanical engineering. He is a Professor and Associate Chair for Administration in the School of Mechanical Engineering and Director of the Rapid Prototyping and Manufacturing Institute at the Georgia Institute of Technology. His research interests include computer-aided design, additive manufacturing, and design methodology.

    Robert B. Stone, Guest Editor 2013–2015, completed his Ph.D. in Mechanical Engineering from The University of Texas at Austin. He is Professor in the School of Mechanical, Industrial and Manufacturing Engineering at Oregon State University. Dr. Stone's research interests include design theories and methodologies, specifically product architectures, functional representations and automated conceptual design techniques, and biologically inspired design.

    Charlie C. L. Wang, Guest Editor 2016–2017, received a B.Eng. degree in mechatronics engineering from Huazhong University of Science and Technology, Wuhan, China. He received his M.Phil. and Ph.D. degrees in mechanical engineering from Hong Kong University of Science and Technology. He is a Professor and Chair of Advanced Manufacturing in the Department of Design Engineering at Delft University of Technology, Delft, The Netherlands. His research interests include geometric computing, computer-aided design, advance manufacturing, and computational physics.

    Yan Wang, Guest Editor 2016 to present, received his B.S. from Tsinghua University, M.S. from Chinese Academy of Sciences, and Ph.D. from the University of Pittsburgh. He is an Associate Professor at the Woodruff School of Mechanical Engineering, Georgia Institute of Technology. His research focus is on modeling and simulation-based design and multiscale systems engineering.

    Christopher Williams, Guest Editor 2014–2017, received a B.S. degree with High Honors at the University of Florida and M.S. and Ph.D. degrees from Georgia Tech, all in mechanical engineering. He is an Associate Professor and J. R. Jones Senior Faculty Fellow at Virginia Tech in the Department of Mechanical Engineering. He also serves as the Associate Director of the Macromolecules Innovation Institute. His expertise is in additive manufacturing (processes and materials), design for additive manufacturing, and engineering design education.
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  • Special Issue: Data-Driven Design (D3)

    Harrison Hyung Min Kim, Ying Liu, Charlie C.L. Wang and Yan Wang
    J. Mech. Des. 2017;139(11):110301-110301-3. doi:10.1115/1.4037943.


    Introduction

    With the arrival of cyber-physical systems or “internet of things” era, massive human- and machine-generated data will create unprecedented challenges and at the same time unmatched opportunities in advancing the theory, methods, tools, and practice of data-driven design for products, systems, and services. By exploiting such huge, versatile, and highly contextualized through-life data, design engineers can harness their organization's competitive edge by uncovering patterns, novel insights, and knowledge for data-driven design. The aim of this special issue is to bring together original and archival articles that present significant contributions in advancing the field of data-driven design.

    The initial idea of the special issue originated from a discussion among Ying Liu, Yan Wang, and Charlie Wang during ASME IDETC/CIE 2016 in Charlotte and was enthusiastically supported by Journal of Mechanical Design's Editor, Dr. Shapour Azarm, and Harrison Kim, who later was invited to join as one of the guest editors. Through a world-wide dissemination of the special issue's call for papers, we received 85 submissions, among which 36 papers were selected for peer review evaluation by a minimum of three reviewers. After a minimum of two rounds of review, 20 papers of different types were accepted for publication, including 18 research papers, one review paper, and one technical brief. The key topics among the accepted papers are: D3 methods (foundation and principles), variability and uncertainty in D3, team dynamic in D3, D3 and lifecycle, and D3 applications and case studies with overarching utilization of data from a wide variety of sources and with different magnitude and sizes. The level of enthusiastic response to our call for papers from the design engineering community confirmed our belief that the community is poised to take a leadership role in advancing knowledge and application domains of D3. Below is provided a short summary of the papers in this issue following the previously mentioned subgrouping of the topics.

    D3 Methods

    The paper, “An Integrated Approach for Design Improvement Based on Analysis of Time-Dependent Product Usage Data” by Ma et al., illustrates that product usage data are used to monitor the performance of functional modules in a hierarchical view. The kernel principal component analysis is applied to reduce the dimension of time-dependent performance feature data set corresponding to functional modules, before Gaussian mixed model fitting is used to model degradation under uncertainty. Health degradation severity of each function thus can be characterized from the distributions. To-be-modified design parameters are then identified from the functions with severe degradation tendencies.

    The paper, “A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval,” by Feng Shi et al., proposes an approach of ontology-based design concept “wordnet” to address some of the current limitations in design document retrieval. The key technique relies on text mining to establish an unsupervised learning ontology network. Validation through an engineering design case study shows that the proposed approach is able to recognize those highly related complex design tasks and their associations with different engineering elements.

    The paper, “V4PCS: Volumetric 4PCS Algorithm for Global Registration,” by Huang et al., provides a geometry registration algorithm that helps identify the similarity between two designs, where the optimum alignment of two surface tessellation models is found efficiently from sampled vertices. The paper shows that the design comparison can support rapid product customization.

    The paper, “A Systematic Function Recommendation Process for Data-Driven Product and Service Design,” by Zhang et al. presents a systematic function recommendation process to suggest new functions to an existing product and service. Different from the conventional approaches where new functions are largely formulated by experienced designers, the proposed approach builds upon recommendation systems that dynamically catch the trendy requirements from targeted users that are not recognized by existing product and service yet. A detailed case study reveals the merits of the proposed approach.

    The paper, “Beyond the Known: Detecting Novel Feasible Domains Over an Unbounded Design Space,” by Chen and Fuge, presents a data-driven adaptive sampling technique—ε-margin sampling—to discover feasible domain in an unbounded design space in an efficient manner. The method both learns the domain boundary of feasible designs, while also expanding the knowledge of the design space as available budget increases. The authors also couple design manifolds with ε-margin sampling to actively expand high-dimensional design spaces without incurring the exponential penalty. The approach is demonstrated in real-world examples of glassware and bottle design cases.

    The paper, “Data-Driven Sizing Specification Utilizing Consumer Text Reviews,” by Chaklader and Parkinson, introduces a new method to determine preliminary design specifications related to human–artifact interaction. The proposed new approach primarily uses text mining of a large number of consumer reviews to suggest human variability information that is essential for interaction. A weighted phrase rating metric is studied which does not require any human intervention but quickly and economically provides information useful to the establishment of design specifications.

    The paper, “Automated Extraction of Function Knowledge From Text,” by Cheong et al., develops a method to automatically extract function knowledge from natural language text. The extraction method uses syntactic rules to extract subject–verb–object triplets from parsed text. Then, the Functional Basis taxonomy, WordNet, and word2vec were leveraged to classify the triplets as artifact-function-energy flow knowledge. The method can find function definitions for 66% of the test artifacts. For those artifacts found, 50% of the function definitions identified are compiled in a well-known design repository. In addition, 75% of the most frequent function definitions found by the method are also defined in the same design repository.

    The paper, “A Convolutional Neural Network Model for Predicting a Product's Function, Given Its Form,” by Dering and Tucker, introduces a deep learning approach based on three-Dimensional Convolutions that predicts functional quantities of digital design concepts. Case studies have been presented in this paper to verify the research questions that are derived from this work, including whether the learned 3D convolutions are able to accurately calculate the functional quantities, determine what the latent features discovered by this network mean, and assess whether the proposed model can perform better than other deep learning approaches.

    The paper, “Mitigating Online Product Rating Biases Through the Discovery of Optimistic, Pessimistic, and Realistic Reviewers,” by Lim and Tucker, offers a new method to lower user rating biases that are caused by customers' optimism or pessimism. By considering the rating history and tendency of a reviewer, the work backed by an unsupervised model aims to adjust the influence on ratings in order to provide customers a more objective and accurate feedback.

    Variability/Uncertainty in D3

    The paper, “Modeling the Variability of Glenoid Geometry in Intact and Osteoarthritic Shoulders,” by de Vries and Parkinson, presents a research work to model the geometric variability of the glenoid of the scapula. The pipeline based on geometric fitting, radial basis functions, and principal component analysis, which can represent the glenoid in a new manner. The work was validated against existing approaches and CT scans from 42 patients. The models created is expected to help surgeons and engineers to understand the effects of osteoarthritis on bone geometry, as well as the range of variability present in healthy shoulders.

    The paper, “A Taylor Expansion Approach for Computing Structural Performance Variation From Population-Based Shape Data,” by Wang and Qian, investigates a Taylor expansion based method for computing structural performance variation over its shape population. To overcome the potential inaccuracy of Taylor expansion for highly nonlinear problems, a multipoint Taylor expansion technique is proposed in the paper, where the parameter space is partitioned into different regions and multiple Taylor expansions are locally conducted. It works especially well when combined with the dimensional reduction of the principal component analysis in the statistical shape modeling.

    The paper, “Mining Process Heuristics from Designer Action Data via Hidden Markov Models,” by McComb et al., shows an application of data-mining techniques to quantitatively study the processes that designers use to solve configuration design problems that are characterized by the assembly of components into a final desired solution. The extraction of human problem-solving heuristics is automated through the application of hidden Markov models, which show that designers proceed through four procedural states in solving configuration design problems.

    The paper, “Predicting Future Importance of Product Features Based on Online Customer Reviews,” by Jiang et al., illustrates that opinion mining is adopted to extract product features from customers' reviews. Fuzzy sets and rules are used to accommodate the imprecision of natural languages. The importance levels or weights of different product features are determined through fuzzified frequencies and sentiment scores. The fuzzy time series method is also applied to predict future importance weights.

    Team Dynamics in D3

    The paper, “Concept Clustering in Design Teams: A Comparison of Human and Machine Clustering,” by Zhang et al., presents a machine learning tool to cluster design concepts and compares the outcome to that of manual clustering. The goal of the clustering algorithm is to support design teams in identifying possible areas of “over-clustering” and/or “under-clustering” in order to enhance divergent concept generation process. The approach was demonstrated by the data generated in a graduate new product development class.

    D3 and Lifecycle

    The paper, “Visual Analytics Tools for Sustainable Lifecycle Design: Current Status, Challenges, and Future Opportunities,” by Ramanujan et al., provides a review of previous research that has created visual analytics tools in sustainable lifecycle design and highlights existing challenges and future opportunities. The opportunities are highlighted for different stages of lifecycle—design, manufacturing, distribution and supply chain, use-phase, and end-of-life.

    The paper, “InnoGPS for Data-Driven Exploration of Design Opportunities and Directions: The Case of Google Driverless Car Project,” by Luo et al., demonstrates that patent mining techniques can be applied to identify technological neighborhoods by analyzing proximity of patent domains in graph models. Future design and technological opportunities can be discovered by adopting the proposed method.

    D3 Applications and Case Studies

    The paper, “Data-Driven Styling: Augmenting Intuition in the Product Design Process Using Holistic Styling Analysis,” by Ranscombe et al., proposes the Holistic Styling Analysis (HSA) for improved digital shape comparison applied to 3D geometry of products. HSA provides objective assessment of difference in appearance to form the basis for designers to rationalize styling to other stakeholders during the design process. The approach enables styling designers to use data to drive their activities in the same manner as other stakeholders. An automotive case study validates the proposed approach by providing objective reference measures for differentiation in multiple products.

    The paper, “Identification of Performance Requirements for Design of Smartphones Based on Analysis of the Collected Operating Data,” by Zhang et al., showcases the case when designers of smartphones analyze the operating data for CPU performance and utilization. A sigmoid like function is used to approximate the cumulative distribution function in order to identify customer satisfaction and the point of cost effectiveness.

    The paper, “Dynamic Data-Driven Design of Lean Premixed Combustors for Thermoacoustically Stable Operations,” by Chattopadhyay et al., uses collected experimental data to generate stability map of combustor in design parameter space. Support vector machine and Markov models are used to identify system states. The relationship between operational conditions and system response is built with a Gaussian process regression. Designers can then use such relationship to perform design optimization.

    The paper, “Mining Patent Precedents for Data-driven Design: The Case of Spherical Rolling Robots,” by Song and Luo proposes a heuristic approach to patent data-driven design and demonstrates the approach in a case study of spherical rolling robot. The approach is an iterative and heuristic methodology to exhaustively search for patents as precedents of the design of a specific technology or product for data-driven next design. Designers can utilize the methodology to make sense of retrieved patent data to explore new design opportunities.

    Finally, the guest editorial team would like to take this opportunity to thank all of the contributing authors for their excellent work. We are also very grateful to the reviewers for offering their precious time and efforts and for providing constructive comments in a timely manner, especially for those submissions that were reviewed for three or even four rounds. Without all of you, this special issue would not be possible. Enjoy reading the papers!
    For the complete special issue visit ASME's Digital Collection
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  • Featured Article: AUTOMATIC ENUMERATION OF FEASIBLE KINEMATIC DIAGRAMS FOR SPLIT HYBRID CONFIGURATIONS WITH A SINGLE PLANETARY GEAR

    Toumadher Barhoumi and Dongsuk Kum
    J. Mech. Des 139(8), 083301; doi: 10.1115/1.4036583 

    Power-split hybrid electric vehicles embody two electric machines in addition to the internal combustion engine, and it employs one or more planetary gear sets (PG) while disposing of the transmission. Most of the prior studies on the design of power-split hybrids focused on finding optimal powertrain configurations, which are configurations specifying the components connections. However, a selected powertrain configuration cannot be physically realized as it does not specify the components arrangements in three dimensional space. Therefore, a given powertrain configuration should be depicted into feasible kinematic diagrams, which are used to generate the three dimensional drawings used for manufacturing. Multiple kinematic diagrams can be depicted for a given powertrain configuration as each kinematic diagrams specifies the exact components arrangements in addition to their connections. In this work, an automatic approach is developed to generate all the feasible kinematic diagrams for any given power-split powertrain configuration with a single PG. First, all the possible components arrangements, i.e. positioning diagrams, are generated. Then, a set of developed feasibility rules are applied on each positioning diagram in order to filter out infeasible components arrangements. Lastly, feasible kinematic diagrams are depicted for each feasible positioning diagram, and a set of preferred design criteria are used to select arrangements that best suit the vehicle’s manufacturability, packaging, maintenance, and cost. The proposed methodology guarantees automatically finding the components arrangements that best suit the desired vehicle through the search of the entire design space.
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    For Full Article visit ASME's Digital Collection
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  • Call For Papers: Special Issue: Design of Engineered Materials and Structures

    The design of engineered materials and structures is a growing and increasingly impactful field of research that intersects materials science, engineering design, engineering mechanics, manufacturing, and data science.  The overarching goal is to accelerate the discovery of new materials for engineering applications.  The approach compliments a traditionally empirical, trial-and-error approach to discovery with an inverse, requirements-driven approach that strategically leverages material databases, simulations and engineering design algorithms and methods to synthesize new materials and structures.  Papers are sought that integrate materials modeling, data collection, simulation, and prediction capabilities with engineering design methods, principles, algorithms, and tools to enable the design of new materials and structures.  To be appropriate for this special issue in the Journal of Mechanical Design (JMD), papers must demonstrate an intellectual emphasis on engineering design. 


    Representative topics include: 
    ·       Simulation-based design methods for enabling accelerated design, development, and insertion of engineering materials
    ·       Integration of theory, simulations, and experiments in the design of materials and structures
    ·       Novel design representation in design of materials and structures
    ·       Multiscale and multiphysics modeling and simulation to support the design of advanced materials and structures
    ·       Data-driven design of materials and structures; data mining and informatics for material and structural design
    ·       Integrated design of products, fabrication processes, and materials
    ·       Topology optimization theory and applications with an emphasis on design of materials and/or integrated design of materials and structures
    ·       Stochastic topology optimization and uncertainty quantification and management for materials and structures
    ·       Manufacturing considerations in design, including in topology, size, and shape optimization
    ·       Rigorous multiscale design, including multiscale topology optimization of materials and structures
    ·       Geometric modeling for design of materials and structures
    ·       Novel materials and structures by design, and their applications

    Submission Instructions
    Please submit your paper to ASME at http://journaltool.asme.org/Content/index.cfm and note on the cover page that your paper is intended for the special issue on “Design of Engineered Materials and Structures”. Please also alert the JMD Editor by email (editor@asmejmd.org) that your paper is intended for the special issue. Information about the Journal of Mechanical Design can be found at http://www.asmejmd.org/. Early submission before the deadline is strongly encouraged.

    Publication Target Dates
    Authors submit papers by:                    March 1, 2018
    Initial review completed by:                   April 15, 2018
    Publication of special issue by:             November 2018

    Early submissions are strongly encouraged.  Papers submitted by March 1, 2018 will be reviewed in time for inclusion in the special issue.  Papers received after that date may still be considered for the special issue, if time and space permit. Papers that are not ready for production in time for inclusion in the special issue may be considered for a regular issue of the journal.  

    Guest Editors
    Carolyn Seepersad, University of Texas at Austin, ccseepersad@mail.utexas.edu
    Raymundo Arroyave, Texas A&M, rarroyave@tamu.edu
    James Guest, Johns Hopkins University, jkguest@jhu.edu
    Andres Tovar, Indiana University-Purdue University Indianapolis, tovara@iupui.edu
    Yan Wang, Georgia Tech, yan.wang@me.gatech.edu
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  • Special Section: Designing for Additive Manufacturing: Recent Advances in Design for Additive Manufacturing

    J. Mech. Des 139(10), 100901; doi: 10.1115/1.4037555 

    Now in its 40th year of existence, ASME's Journal of Mechanical Design has covered a wide range of topics on behalf of the Design Engineering Division. The past 40 years have seen countless advances in mechanical design, developing new knowledge in areas ranging from simulation to representation to communication, among others. These advances have often been complemented by similar advances in manufacturing, and traditional manufacturing processes such as machining and injection molding have been investigated heavily by the engineering design community. Today, however, we are in the midst of a paradigm shift. Whereas design methods in the past sought to overcome the design constraints imposed by manufacturing technologies, emerging digital manufacturing processes are removing many of these barriers and introducing new ones that are not yet fully understood. As a result, the additional degrees-of-freedom offered via selective (multi-) material addition/subtraction have exceeded our current design proficiencies. Additive manufacturing (AM) is at the forefront of this shift, and our engineering design software, methods, and tools are struggling to keep pace.

    As many readers know, AM provides unprecedented freedom for designing and engineering parts that are fabricated layer-by-layer. AM enables novel designs for a wide array of uses and applications in a range of industries, including aerospace, consumer goods, defense, energy, and medical, among others. Components can be easily light-weighted with topology optimization and lattice structures, complex assemblies can be consolidated into single 3D-printed geometries to reduce manufacturing complexity, and multimaterial fabrication techniques made possible by several AM processes enable never before seen functionally graded materials. In short, AM is changing not only what we design but also how we design, and a recent National Science Foundation Workshop on Additive Manufacturing Education and Training revealed that Design for Additive Manufacturing was the most pressing need for (re)training the engineering workforce. Consequently, this Special Section explores recent advances in the theories, methods, tools, and guidelines in Designing for Additive Manufacturing (DFAM). These contributions are empowering engineers to design and realize new parts, products, and systems that leverage AM processes' full capabilities, and in turn, are accelerating the adoption and application of AM technology.

    This Special Section is the second of its kind within the Journal of Mechanical Design, following the 2015 Special Issue: Design for Additive Manufacturing: A Paradigm Shift in Design, Fabrication, and Qualification. Since the previous issue, AM has maintained a high level of interest and continues to flourish as design and manufacturing technology have advanced at a feverish pace. America Makes, the first Manufacturing USA Institute, remains a strong advocate for AM technology, providing numerous partnership opportunities for industry and academia to join forces to help accelerate AM adoption. The Additive Manufacturing Standards Collaborative has documented the needs for Design for AM standards and development. Meanwhile, DARPA's transformative design (TRADES) program was established to advance the foundational mathematics and computational tools required to generate and better manage the enormous complexity of design in today's increasingly digital manufacturing environment. Finally, companies like Autodesk, Dassault, Parametric Technologies Corporation, and Siemens are in a neck-and-neck race to field integrated computer-aided design, modeling/simulation, and process planning software support for AM.

    Like the previous special issue, we have aimed to present readers with state-of-the-art research regarding DFAM in this special section. The papers in this special section can be categorized into three broad categories: (1) Review of State-of-the-Art, (2) Advances in State-of-the-Art, and (3) DFAM Case studies. Together, these papers highlight the advancements made in the past 2 years in DFAM in the engineering design community. This snapshot of where we currently stand as a design community, and how AM technologies are driving advances in new design paradigms and industrial applications, demonstrates how far we have come within a short period of time. The industrial adoption of AM continues to expand, with numerous companies now using AM processes to produce end-use artifacts in large quantities. AM technologies and material capabilities have continued to rapidly improve, and in turn, have continued to spur new opportunities for design theory, methodology, and automation.

    We expect that this will not be the last Special Issue or Special Section on Design for Additive Manufacturing—only the latest. As industry increasingly recognizes AM as viable production technology and integrates it within their existing manufacturing process chain, the need for expanding the mechanical design capabilities for engineers is sure to follow.

    Guest Editors
    Timothy W. Simpson, Pennsylvania State University, tws8@psu.edu
    Jesse Boyer, Pratt & Whitney, jesse.boyer@pw.utc.com
    Carolyn Seepersad, University of Texas, Austin, ccseepersad@mail.utexas.edu
    Christopher B. Williams, Virginia Tech, cbwilliams@vt.edu
    Paul Witherell, NIST, paul.witherell@nist.gov
    For the Full Special Section visit ASME's Digital Collection
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  • Featured Article: Cyber-Empathic Design: A Data-Driven Framework for Product Design

    Dipanjan Ghosh, Andrew Olewnik, Kemper Lewis, Junghan Kim and Arun Lakshmanan
    J. Mech. Des 139(9), 091401 (Jul 12, 2017); doi: 10.1115/1.4036780 


    Understanding consumer perceptions of products and the potential impact of those perceptions on purchase decisions is critical information that should influence product development decisions. Though firms often seek consumer feedback on products, such feedback often occurs long after product use and lacks specific details about the interaction, usage context, etc. This work introduces a novel framework – Cyber-Empathic Design – that integrates sensor data and real-time user feedback to develop a more accurate model of user perceptions. The framework is applied to a case study focused on user perceptions of shoes. The results of this work demonstrate the potential for product developers to leverage the IoT (internet-of-things) movement, real-time user feedback, and advances in machine learning to connect user perceptions to specific engineered product features. 
    Figure: Data collection method (left) and resulting perceptual model (right). 
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    For the Full Article visit ASME's Digital Collection.
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  • Featured Article: Mining Process Heuristics from Designer Action Data Hidden Markov Models

    Christopher McComb, Jonathan Cagan and Kenneth Kotovsky
    ASME doi:10.1115/1.4037308

    Configuration design problems are common in everyday life as well as engineering, with examples ranging from the selection and arrangement of furniture for a living room to the type of problem-solving used by NASA engineers to return Apollo 13 safely to Earth. There are many theoretical approaches for solving configuration design problems but few studies have examined how humans naturally solve them. This work used data-mining techniques (specifically hidden Markov models) to study the behavioral patterns shown by humans solving two distinct configuration design problems. Mining this data revealed beneficial process heuristics that are potentially generalizable to the entire class of configuration design problems. The trained models indicate that designers proceed through four procedural states, beginning in a state dominated by topology design and progressing to a final state with a focus on parameter design. The mined models also indicate that high-performing designers opportunistically tune parameters early in the process, enabling a more effective and nuanced search for good solutions.
    For the full article please visit ASME's Digital Collection.
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  • Editors' Choice Paper Awards Process and Outcomes

    I am pleased to announce the Journal of Mechanical Design's (JMD's) Editors' Choice Paper Awards for the years: 2014, 2015, and 2016.

    Back in May 2014, I wrote an Editorial in JMD titled: “Announcing JMD's Annual Best Paper Award Guidelines.” In that editorial, I outlined the procedures we planned to follow to choose a yearly best paper from papers published in JMD in that year. However, after much further thought and input from the design engineering community, including many JMD Associate and Guest Editors and Editors of other ASME journals, I have decided to call this award: “Editors' Choice Paper Award.” The words “Editors' Choice” were used to refer to the Associate Editors (AEs) and Guest Editors (GEs) involvement in the nomination and selection process.

    Let me review the process of selecting the editors' choice paper(s). First, the AEs and GEs involved with the journal in a particular year were asked to nominate papers from those published in JMD in that year. Next, AEs and GEs were asked to vote on the nominated papers. Finally, a three-member committee from current and/or former AEs was formed to finalize the selection. The charge to the committee was to select one or more papers from JMD papers published in each of the years 2014, 2015, and 2016 and which were nominated and voted on by AEs/GEs.

    As stated in my 2014 Editorial, the selection criteria used were based on (i) fundamental value of the contribution, (ii) expectation of archival value (e.g., expected number of citations), (iii) practical relevance to mechanical design, and (iv) quality of presentation. The selection committee informed me that in addition to these criteria, it considered the following two criteria: “The breadth of interest and applicability” and “whether the paper addressed an emerging area or an area of immediate interest in the community.” With its final selection, the committee also indicated that: “while we had strong consensus around the papers that were selected, we wanted to make sure that taken as a body the three papers represented a variety of communities and interests (e.g., we did not want three optimization papers or three gear train papers).”

    I am now pleased to inform you that the selection committee has finalized and informed me of their selection of Editors' Choice Paper Awards for each of the years 2014–2016, as listed below:



    1. --2014 Editors' Choice Paper Award: “A Descriptor-Based Design Methodology for Developing Heterogeneous Microstructural Materials System,” Hongyi Xu, Yang Li, Catherine Brinson, and Wei Chen, J. Mech. Des., 2014, 136(5): 051007–051007-12.
    2. --2015 Editors' Choice Paper Award: “Level Set Topology Optimization of Printed Active Composites,” Kurt Maute, Anton Tkachuk, Jiangtao Wu, H. Jerry Qi, Zhen Ding, and Martin L. Dunn, J. Mech. Des., 2015, 137(11): 111402–111402-13.
    3. --2016 Editors' Choice Paper Award: “A Stiffness Formulation for Spline Joints,” J. Hong, D. Talbot, and A. Kahraman, J. Mech. Des., 2016, 138(4):043301–043301-8.




    Each author of these papers will receive a plaque in recognition for their award. Also, all three papers can now be accessed FREE online at ASME's Digital Collection homepage.

    Please join me in congratulating the authors of these papers. I also would like to take this opportunity to thank all of the current and former AEs and GEs who participated in the nomination of the papers and voted on them. In particular, I want to thank the selection committee who had to carefully read through a large number of the papers and collectively make their final choices!

    While I anticipate that there will be room for improvement in the selection process, I am hoping that the Editors' Choice Paper Award becomes a JMD tradition and an annual event.
     
    Shapour Azarm, Technical Editor

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  • Featured Article: Optimal Design of Panel Reinforcements With Ribs Made of Plates

    Shanglong Zhang and Julián A. Norato
    J. Mech. Des 139(8), 081403 (2017); doi: 10.1115/1.4036999 

    Reinforcing ribs can significantly increase the stiffness of panels. In this study, we formulate a computational design method to determine the optimal position, dimensions and orientation of ribs made of stock plates and welded to a panel to maximize its stiffness. Typical applications of welded rib reinforcements are large metallic structures with low production volumes, for which other processes such as machining or stamping are either infeasible or too costly.  These applications include, for example, ship hulls, fuel tanks, aircraft wing structures and linkage components in heavy machinery. To determine the optimal ribs layout, we formulate a topology optimization technique whereby a feature-based geometric representation of the rib is smoothly mapped onto a finite element mesh for analysis. This mesh remains fixed throughout the optimization, thus circumventing re-meshing upon changes in the ribs layout. Importantly, our method enforces geometric constraints to ensure manufacturability, namely that: a) ribs must remain vertical at all times to ensure a good quality weld; b) the ribs dimensions must not exceed those of available stock plates; c) ribs should not encroach the space above holes on the panel used for routing other components or for access; and d) there must be a minimum spacing between ribs to ensure adequate access for the welding gun. Ours is the first method to determine the optimal layout of welded ribs made of flat plates within a 3-dimensional design envelope that satisfies the foregoing geometric constraints.
    For the full article please visit ASME's Digital Collection.
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  • Featured Article: Thumb Configuration and Performance Evaluation for Dexterous Robotic Hand Design

    Hairong Wang; Shaowei Fan; Hong Liu
    J. Mech. Des. 2016; 139(1):012304-012304-12
    doi: 10.1115/1.4034837

    The force and/or motion transmissibility and the analyticity of inverse kinematics for a thumb mechanism depend on thumb configuration. This paper presents a general framework for the thumb configuration and performance evaluation in the design of dexterous robotic hand. The thumb configuration is described by the functional analysis of human thumb, and the thumb of robotic hand is generalized into fifteen configurations. A performance evaluation method is proposed based on kinetostatic and dynamic dexterity as well as workspace. The kinetostatic dexterity is based on a Jacobian matrix condition number. A dynamic dexterity measure is presented via acceleration analysis, which keeps a clear geometric meaning. The proposed method is applied to evaluate the performance of three examples, which cover thumb configurations of most existing dexterous hands. Performance evaluation results demonstrate the effectiveness of the proposed method. Using these results and the proposed performance evaluation method, meaningful design principles are presented to guide the design of the thumb configuration.
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  • Featured Article: Inspiration and Fixation: The Influences of Example Designs and System Properties in Idea Generation

    Luis A. Vasconcelos; Carlos C. Cardoso; Maria Sääksjärvi; Chih-Chun Chen; Nathan Crilly
    J. Mech. Des. 2017; 139(3):031101-031101-13
    doi: 10.1115/1.4035540

    External inspiration stimuli can be very effective to help designers arrive at new ideas that they would be otherwise unlikely to generate. However, exposure to external stimuli can also hinder creativity and fixate designers on particular features of such stimuli. We conducted an experiment with novice designers to compare the inspiration effects from two stimuli types: a concrete example solution (a bike) and an abstract property that a solution might incorporate (modularity). Working alone in a short design session, participants were asked to generate ideas to eliminate the need for people to have multiple bikes as they grow up. We found that exposure to either the concrete example or the abstract property reduced the total number of ideas generated and how diverse those ideas were, and that exposure to both stimuli (together) reduced these measures even further. We also found that each stimulus affected participants differently, encouraging ideas like one type of stimulus, while discouraging ideas like the other type. These findings reinforce the idea that external stimuli can hinder creativity and should be accessed carefully. They also show how concrete and abstract stimuli can produce similar inspiration effects, challenging our intuitions about how to encourage wide-ranging ideas. This has the potential to shape how design is taught and how inspiration tools are developed.
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    Full the full paper please visit ASME's Digital Collection.
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  • Featured Article: Development and Evaluation of a Mechanical Stance-Controlled Orthotic Knee Joint with Stance Flexion


    Jan Andrysek; Matthew J. Leineweber; Hankyu Lee
    J. Mech. Des. 2017; 139(3):035001-035001-7
    doi: 10.1115/1.4035372

    People with severe impairment of the lower body caused by conditions such as polio or stroke often rely on assistive devices for mobility. Knee orthosis plays an important role in restoring mobility by stabilizing the weakened lower limb and providing support for standing and walking. Concurrently, the orthosis should allow for natural and efficient movement of the limb as required for walking. The focus of this work is to develop a new method for controlling orthotic knee joints. The new control method uses a mechanical system to monitor loading and timing events and patterns, and apply knee-locking function when the limb is loaded. A prototype was built and tested on a polio patient and demonstrated the feasibility of this approach for providing reliable orthotic function. Further work aims to test the knee joint on a larger group of individuals within the community.

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    For the full paper please visit ASME's Digital Collection.
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  • Featured Article: Design and Characterization of a Continuous Rotary Minimotor Based on Shape Memory Wires and Overrunning Clutches

    Giovanni Scirè Mammano and Eugenio Dragoni
    J. Mech. Des 139(1), 015001; doi: 10.1115/1.4034401

    An attractive but little explored field of application of the shape memory technology is the area of rotary actuators, in particular for generating endless motion. This paper presents a miniature rotary motor based on shape memory alloy (SMA) wires and overrunning clutches which produces high output torque and unlimited rotation. The concept features a SMA wire tightly wound around a low-friction cylindrical drum to convert wire strains into large rotations within a compact package. The seesaw motion of the drum ensuing from repeated contraction-elongation cycles of the wire is converted into unidirectional motion of the output shaft by an overrunning clutch fitted between drum and shaft. Following a design process formerly developed by the authors, a six-stage prototype with size envelope of 48´22´30 mm is built and tested. Diverse supply strategies are implemented to optimize either the output torque or the speed regularity of the motor with the following results: maximum torque = 20 Nmm; specific torque = 6.31´10-4 Nmm/mm3; rotation per module = 15 deg/cycle; free continuous speed = 4.4 rpm.
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  • GUEST EDITORIAL SPECIAL ISSUE: SIMULATION-BASED DESIGN UNDER UNCERTAINTY

    November, 2016 | Volume 138 | Issue 11

    Uncertainty quantification and propagation using probabilistic and nonprobabilistic methods are essential in many engineering and nonengineering disciplines. In mechanical design, there is an ever-increasing need to design systems considering uncertainty and variability using simulation models. The past decade has seen a significant growth in uncertainty quantification, propagation, and design. The “Simulation-Based Design Under Uncertainty” special session of the ASME Design Automation Conference (DAC) has been attracting many papers every year for more than twelve years. Design under uncertainty has implications in decision-making as well as reliability, quality, safety, and risk tolerance of many products. This special issue covers various related topics under the general umbrella of simulation-based design under uncertainty, including methods, models, and case studies.

    The idea for the special issue came from the Editor, Dr. Shapour Azarm, who expressed the need to highlight recent advances in the area of design under uncertainty covering all the aspects of uncertainty quantification, propagation, and significance in design. Considering the year-after-year interest from both academia and industry in the “Simulation-Based Design Under Uncertainty” special session in DAC, the interest from other ASME Design Engineering Conferences in design under uncertainty, and the ever-increasing need to design mechanical and structural systems for reliability, quality, safety, and risk tolerance, we were immediately and enthusiastically supportive and responsive to Dr. Azarm's initiative. We would like to thank him for that.

    The papers in this special issue were submitted to the ASME Journal of Mechanical Design in response to a Call for Papers (CFP) issued in November 2015 with a submission deadline of late April 2016. We received 58 papers among which 19 went through at least two rounds of review following the guidelines and standard review procedures of the journal. The 12 full papers and 2 technical briefs that are presented in this special issue highlight developments in uncertainty propagation and design using probabilistic and nonprobabilistic methods, uncertainty reduction and risk management in design, algorithms for robust design, model validation, design for resilience, multidisciplinary analysis and optimization under uncertainty, time-dependent reliability, system reliability and redundancy allocation, and additive manufacturing.

    Below are the highlights of all the papers in this special issue. Y. Zhang, M. Li, J. Zhang, and G. Li proposed a new robust optimization (RO) framework using Gaussian processes, considering not only parameter uncertainty but also model uncertainty to represent the uncertainty in simulation models. Model uncertainty in RO can reduce the risk of the obtained robust optimal designs becoming infeasible even if the parameter uncertainty is considered.

    Another paper, co-authored by S. M. Göhler, T. Eifler, and T. J. Howard, presented a review of robustness metrics and classified all the metrics based on the information necessary to calculate them. The authors identified four different classes of robustness metrics using sensitivity, size of feasible design space, functional expectancy and dispersion, and probability of compliance information. The paper aimed at providing a comprehensive overview of robustness metrics as well as guidance to understand the different types of robustness metrics and remove potential ambiguities associated with the term robustness.

    A methodology to decide the degree of conservativeness in an initial design considering the risk of future redesign was presented by N. B. Price, N.-H. Kim, R. T. Haftka, M. Balesdent, S. Defoort, and R. Le Riche. While early in the design process, there is often mixed epistemic model uncertainty and aleatory parameter uncertainty, and later in the design process, the results of high-fidelity simulations or experiments reduce epistemic model uncertainty and may trigger a redesign process. The authors proposed a margin-based design/redesign method where the design is optimized deterministically, but the margins are selected probabilistically. Their method allows for tradeoff between expected final design performance and probability of redesign while ensuring reliability with respect to mixed uncertainties.

    T. Xia, M. Li, and J. Zhou continued on the subject of robustness proposing a sequential robust optimization approach for multidisciplinary design optimization for problems with both aleatory and epistemic uncertainties represented by intervals. The proposed approach obtains first a solution by giving full autonomy to the subsystems using a tolerance range for the coupling variables in order to propagate uncertainty in the coupled system. Then, an auxiliary sequential optimization process is implemented to get the optimal robust solution.

    Another paper by P. Pandita, I. Bilionis, and J. Panchal contributed to the important area of stochastic optimization of high-dimensional problems with expensive black-box functions. Design optimization under uncertainty is notoriously difficult for problems with an expensive objective function. The paper used concepts of Bayesian global optimization (BGO) to alleviate the high cost of information acquisition and select sequential simulations optimally. It reformulated the expected improvement (EI) information acquisition function (IAF) in BGO to filter out parametric and measurement uncertainties. The proposed approach alleviates the effect of the curse of dimensionality using a fully Bayesian interpretation of Gaussian processes and adaptive Markov chain Monte Carlo to improve robustness.

    Two papers addressed the timely area of engineering resilience quantification and assessment and their implications in system design. Engineering resilience has recently gained popularity. However, there are different opinions among designers, engineers, practitioners, and policy makers on what is engineering resilience, and how it can be quantified, designed, and implemented in engineering and nonengineering systems.

    In the first paper on engineering resilience, N. Yodo and P. Wang presented a literature survey on existing engineering resilience studies from a system design perspective. Focusing on engineering resilience metrics and their design implications, the authors provided a definition of engineering resilience and proposed resilience quantification metrics, analysis methodologies, and design tools which can be applicable for a broad range of engineering systems.

    The second paper, co-authored by Z. Hu and S. Mahadevan, proposed a new resilience metric using time-dependent system reliability concepts and described a methodology to design a system which meets a specific system resilience target. The time-dependent reliability analysis is used to identify the dominant system failure paths involved in the estimation of system resilience. A sensitivity analysis is also presented to identify the important design parameters which affect the system resilience.

    J. Wang and M. Li proposed a redundancy allocation approach to increase the reliability of multistate systems with component dependencies and load sharing. These two features, present in most real-world applications, have hindered the development of successful redundancy allocation algorithms. A novel redundancy allocation scheme based on a semi-Markov model and optimization is proposed and successfully demonstrated on several two-component problems. The authors concluded that their approach constitutes an important first step but further developments are needed for systems with an arbitrary number of subsystems.

    M. M. J. Opgenoord, D. L. Allaire, and K. E. Willcox presented a variance-based sensitivity approach for simulation-based design under uncertainty to reduce the effect of input uncertainties on the output. Global sensitivity analysis (GSA) methods are typically used to rank input variables and eventually select the important ones. The authors proposed a distributional sensitivity analysis (DSA), which, similarly to GSA, enables the ranking of the input variables, and in addition, links the output variance as a function of the uncertainty reduction of the input variables. With this information, the designer can target the reduction of uncertainty associated with a specific input. The approach is applied to the design of a commercial airliner.

    An approach to propagate model uncertainties in multidisciplinary analysis was presented by S. Dubreuil, N. Bartoli, C. Gogu, and T. Lefebvre. The uncertainties are modeled as random fields using a polynomial chaos expansion constructed over the design and coupling variables space. The proposed approach results in a semi-intrusive formulation with emphasis on approximating each discipline using a Kriging metamodel. The latter represents intrinsically the model uncertainties. Among other examples, the developments are demonstrated on two conceptual aircraft design problems.

    A model validation method for dynamic engineering models under uncertainty was presented by Z. Wang, Y. Fu, R.-J. Yang, S. Barbat, and W. Chen. Validating dynamic engineering models is an important topic in simulation-based design under uncertainty. Although significant progress has been made, the existing metrics lack the capability of managing uncertainty in both simulations and experiments. This paper presented an area-based metric to systemically handle uncertainty and validated computational models for dynamic systems over an input space by simultaneously integrating the information from multiple validation sites. A truncated Karhunen–Loève (KL) expansion represents the responses of the dynamic system in order to manage the complexity associated with a high-dimensional data space.

    L. Brevault, S. Lacaze, M. Balesdent, and S. Missoum presented a reliability analysis in the presence of both aleatory and epistemic uncertainties. Their methodology is then applied to predict the fallout zone of a launch vehicle. An interval framework is used to quantify the epistemic uncertainties. The proposed method allows designers to determine the bounds of the failure probability and involves a sequential approach using subset simulation, Kriging, and an optimization process. To reduce the simulation cost, a refinement strategy of the surrogate model is proposed taking into account the presence of both aleatory and epistemic uncertainties.

    Finally, this special issue includes two technical briefs. In the first one, J. Zhou, M. Xu, and M. Li proposed a reliability-based design optimization (RBDO) method under mixed probabilistic (aleatory) and interval (epistemic) uncertainties considering the variation of the objective because of the uncertainties. The authors proposed a single-loop robust optimization approach to efficiently calculate the worst-case solution due to the interval uncertainty using the Utopian solution. The remaining problem is then solved using existing reliability-based design optimization (RBDO) methods. An example demonstrates the applicability of their approach and illustrates the necessity to consider the variation of the objective for robustness reasons.

    The second technical brief, co-authored by F. Lopez, P. Witherell, and B. Lane, discussed the origin and propagation of uncertainties in additive manufacturing focusing on models for laser powder bed fusion (L-PBF). Modeling assumptions, unknown simulation parameters, numerical approximations, and measurement error are considered. The quantification and algorithms for the reduction of each source of uncertainty are discussed. A case study of a thermal model for predicting the melt pool width was presented.

    We hope these papers stimulate further research ideas and advances in the area of simulation-based design under uncertainty. We sincerely thank all the authors who responded to our CFP, whether or not their paper appeared in the special issue. We also thank all the reviewers for their prompt response to our multiple requests and short deadlines. Last but not least, we would like to thank Ms. Amy Suski who patiently and effectively helped us with many logistical details.
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    Mian Li, Guest Editor
    University of Michigan-Shanghai Jiao Tong University
    Joint Institute,
    Shanghai 200240, China 


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    Sankaran Mahadevan Guest Editor
    Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, TN 37235 


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    Samy Missoum, Guest Editor
    Department of Aerospace and Mechanical Engineering,
    University of Arizona, Tucson, AZ 85721 


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    Zissimos P. Mourelatos, Guest Editor
    Department of Mechanical Engineering,
    Oakland University, Rochester, MI 48309
    For the Full Special Issue please visit ASME's Digital Collection.
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  • Featured Article: The Effects of Gender and Idea Goodness on Ownership Bias in Engineering Design Education

    Christine A. Toh, Andrew A. Strohmetz and Scarlett R. Miller
    J. Mech. Des 138(10), 101105; doi: 10.1115/1.4034107 

    Concept selection is a critical stage of the engineering design process because of its potential to influence the direction of the final design. While formalized selection methods have been developed to increase its effectiveness and reduce human decision-making biases, research that understands these biases in more detail can provide a foundation for improving the selection process. One important bias that occurs during this process is ownership bias, or an unintentional preference for an individuals’ own ideas over the ideas of others. However, few studies have explored ownership bias in a design setting and the influence of other factors such as the gender of the designer or the “goodness” of an idea. In order to understand the impact of these factors in engineering design education, a study was conducted with 110 engineering students. The results from this study show that male students tend to show ownership bias during concept selection by selecting more of their own ideas while female students tend to show the opposite bias, the Halo Effect, by selecting more of their team members’ concepts. In addition, participants exhibited ownership bias for ideas that were considered good or high quality, but the opposite bias for ideas that were not considered good or high quality. These results add to our understanding of the factors that impact team concept selection and provide empirical evidence of the occurrence of ownership bias and the effects of gender and idea goodness in engineering design education. 

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  • Special Issue Guest Editorial: Design Theory and Methodology

    ASME, Journal of Mechanical Design (October 2016)
    doi:10.1115/1.4034269 

    To create a journal that addressed a breadth of topics like vibration, wear, impact, and fatigue in mechanical components – it may have seemed logical that this journal was born in the late 1970s under a rather generic moniker of “mechanical design”. But over its 39 years of existence, the journal grew quickly to cover broader notions of mechanical design – notions covering the mechanical designprocess as well as mechanical design products. In the late 1980s, the focus on the design process alone had accumulated enough interest to lead several researchers to dedicate an entire conference to the theories and methodologies of design. The first ASME DTM (Design Theory & Methodology) Conference took place in Montreal in the late summer of 1989. It included 13 papers divided into three themes that have since blossomed, merged, divided, and persevered: 1) Design Process Evaluation, 2) Knowledge Representation and Design Process, and 3) Employing Computation in Design. This year, the ASME DTM conference will celebrate its 27th year with a dozen themes and 48 papers. Participants to the annual international conference will attest that it is always a well‐attended (usually standing room only) and enthusiastic conversation about the mechanical engineering design process. Clearly, engineering design is essential to the success of any industry endeavor – whether it be the success of a particular engineering firm, the success of a public infrastructure project, or the success of a high‐tech invention. And, this DTM community has sought to explicitly define the extent and underlying common phenomena of the engineering design process. It is clear that in addition to understanding our products and the underlying physics that govern their success, we – as engineers – must understand our design process and the theories and methods that define it and push it forward. 

    In celebration of the Design Theory and Methodology conference, the recent conference and committee chairs have worked together to create this special issue of JMD with a focus on themes commonly depicted in the conference. In fact, several of these special issue papers appeared in an earlier form at the conference, while others were incorporated to round out the theme and all of it subgenres. We would also like to thank Associate Editor Carolyn Seepersad for coordinating the reviews for one of the papers in this special issue. 

    As a part of this special issue, we would also like to acknowledge the tremendous contributions Professor Clive Dym made to our community and engineering design education. His career spanned over 40 years. Professor Dym was a renowned engineering educator and researcher inspiring many students and faculty. He transformed engineering design education including as lead author on the book Engineering Design: A Project‐Based Introduction, which is used by many universities to teach engineering design. He was professor emeritus of Engineering Design and Director of the Center for Design Education at Harvey Mudd College, USA. He held many honors including the Fletcher Jones Design Chair, and the National Academy of Engineering Gordon Prize for his contributions to engineering design education. He continued to have impact throughout his life with his most recent contribution igniting the North American Chapter of the Design Society to help better connect the design theory and methods community in ASME to the broader international community. He mostly received the inaugural Design Theory and Methodology award and was invited as a guest editor for this special issue.

    Several of the papers in this issue present new design methods. The paper by Hahn, Marconnet, and Reid presents a seven‐step framework for determining customer needs from DIY practitioners. The approach is illustrated for lead users in the hair care industry. Königseder and Shea present a new method for supporting computational design synthesis development and application and include two case studies. In “Design for Sustainable Use of Appliances: A Framework Based on User Behavior Observations,” energy overuse is presented as a failure mode brought about by user behavior. By understanding why and how users overuse energy in devices, an engineering designer can perhaps change a design to prevent this overuse. “Design Roadmapping: A Framework and Case Study on Planning Development of High‐tech Products in Silicon Valley” describes a framework that parallels existing product and technology roadmapping processes to overcome differences due to customer needs not being considered with technology choices. 

    Other papers focus on understanding phenomena related to design – whether it be on the part of the designer or the end‐user. The paper, “The Effects of Gender and Idea Goodness on Ownership Bias in Engineering Design Education,” evaluates biases that affect decisions during the design process. In this paper, the authors show how male designers have a larger ownership bias toward their own ideas and women are more likely to prefer the designs by their team members. The paper, “Effect of Product Representation in Visual Conjoint Analysis,” is an interesting study showing how representation will affect the consumer preference of design options. The authors find user preference towards particular features varied depending if a sketch, solid model or a prototype of the product was shown to the users. The paper, “Discovery of Mental Metadata used for Analogy Formation in Function‐Based Design,” furthers our understanding of design by analogy. In particular, it finds from protocol studies with engineering students that in addition to previously researched functional analogies, flow analogies are common and useful during design. This new finding is helpful in developing computational design support systems for designers. 

    Finally, a set of papers look at existing design methods in hopes of understanding their interactions and effectiveness. Daly, Seifert, Yilmaz, and Gonzalez compared three techniques for engineering idea generation: Design Heuristics, Morphological Analysis, and Individual Brainstorming ‐‐ illustrating the strengths of each technique and exposing the value of using multiple approaches for idea generation. Gericke, Kramer, and Roschuni examined how designers discover and adopt design methods. Through a thorough interview process, the authors draw conclusions on how one might best present design methods online to help design practitioners find methods useful to them. Finally, “Design Principles: Literature Review, Analysis, And Future Directions” is a literature review collaboration authored by three reputable professors that reflects on four decades of research on the engineering design process. Through the retrospective, the authors formally define and categorize important aspects of this growing corpus of design theory and methodology research.
    Matthew I. Campbell
    Oregon State University
    Katja Hölttä‐Otto
    Aalto University
    Julie Linsey
    Georgia Institute of Technology
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    The Special Issue on Design Theory and Methodology (October 2016 issue of JMD) is available on ASME's Digital Collection.
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  • Cityplot: Visualization of High-Dimensional Design Spaces With Multiple Criteria

    J. Mech. Des 138(9), 091403; doi: 10.1115/1.4033987​

    Cityplot is a new visualization technique for engineering design that uses a dimensionally-reduced representation of the design decisions to represent the mapping from the decisions to the criteria upon which a design is judged. The shown Cityplot depicts possible CubeSat constellations to support the 2007 Earth Science Decadal Survey. Each constellation is comprised of up to 4 CubeSats and each CubeSat can select from a list of 7 instruments. Possible CubeSat constellations are “cities” and are placed in a 2d space to be visualized. An individual constellation can also be seen as a table of instruments (rows) being present (black) on a given CubeSat (columns). The benefits, costs and risks of each possible constellation are represented as color-coded “buildings” in each “city”. The criteria in this example are: a tiered count of satisfied Decadal objectives (blue), the average CubeSat Technology Readiness Level (red), lifecycle cost (green), maximum number of lost instruments upon loss of a single satellite (black). A taller building indicates the possible constellation performs better in that criteria. Dark purple “roads” between two designs indicate that only one instrument is either added to or removed from one CubeSat to make one constellation identical to the other. Cityplot simultaneously shows sensitivity of criteria to decisions, criteria tradeoffs and design families via a quick intuitive view of the design space.
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    For the Full Article please see ASME's Digital Collection.
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  • In Memoriam

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    Dr. Clive L. Dym (1942 - 2016)
    Dr. Clive L. Dym, the world-renowned design educator and Harvey Mudd College engineering Professor Emeritus, passed away on May 3, 2016.

    Dr. Dym received his B.S. degree in Civil Engineering from The Cooper Union in 1962, his M.S. degree in Applied Mechanics from Polytechnic Institute of Brooklyn in 1964, and his Ph.D. degree in Aeronautics and Astronautics Engineering from Stanford University in 1967. His vast professional experience began at the State University of New York at Buffalo, where he served as an Assistant Professor for the program in Policy Sciences and also for Engineering and Applied Sciences until 1969. He then worked at the Institute for Defense Analysis (IDA) until 1970. After IDA, he served as an Associate Professor of Civil Engineering at Carnegie-Mellon University (CMU) until 1974. During 1974-1977, he worked as a Senior Scientist with Bolt Beranek at Newman, Inc. He then joined the Department of Civil Engineering of the University of Massachusetts as Professor (1977-1991) and Head (1977-1985). In 1991, he moved his academic career to Harvey Mudd College as the inaugural holder of the Fletcher Jones Design Chair, Director of Center for Design Education, and Chair of Department of Engineering.

    Dr. Dym had many other appointments including Visiting Professorship in Civil Engineering at Stanford University, CMU, and Northwestern University. He received numerous peer recognitions and awards including elected Fellow of the American Society of Engineering Education (ASEE), American Society of Mechanical Engineers (ASME), American Society of Civil Engineers (ASCE), and Acoustical Society of America (ACS). He also received ASEE’s Archie Higdon Distinguished Educator Award, ASEE’s Fred Merryfield Design Award, ASME’s Ruth and Joel Spira Outstanding Design Educator Award, Boeing’s Outstanding Engineering Educator Award, and the National Academy of Engineering’s Gordon Prize for which his citation reads: “Creating and disseminating innovations in undergraduate engineering design education to develop engineering leaders.” Dr. Dym’s professional activities included serving as Founding Editor of Artificial Intelligence for Engineering Design, Analysis and Manufacturing, and as an Editorial Board Member of International Journal of Engineering Education, ASME Journal of Mechanical Design, Journal of Sound and Vibration, Noise Control Engineering Journal, Journal of the Acoustical Society of America, and Shock and Vibration Digest.

    Due to his passion in engineering design education, Dr. Dym started a biennial program of workshops which is now named in his honor as the Clive L. Dym Mudd Design Workshops. Dr. Gordon Krauss, his colleague at Harvey Mudd, notes that: “his influence in design education has been far-reaching. His work made teaching design early in undergraduate engineering education move from impossible to necessary to the benefit of all involved. I believe that the push for design thinking in K-12 education is simply an extension of his pioneering work.” Dr. Dym’s own perspective on this is captured in an NSF interview with pioneers in the field here: http://depts.washington.edu/celtweb/pioneers-wp/?p=489

    Clive Dym will be greatly missed by his friends and colleagues in many fields including the engineering design community around the world. His game-changing contributions to engineering design education will continue to have a lasting influence and provide enormous inspiration in the years to come.

    Dr. Clive Dym is survived by his family in the United States and Israel, including his wife, Joan Dym and daughters, Jordana and Miriam Dym.

    Following this memorial, a listing of some select publications including books and papers by Dr. Clive L. Dym is provided.

    Shapour Azarm
    Editor, Journal of Mechanical Design
     
    BOOKS
    1.       Clive L. Dym and Irving H. Shames, Solid Mechanics: A Variational Approach, McGraw-Hill Book Company, New York, 1973. (Reprinted as International Student Edition, McGraw-Hill Kogakusha, Ltd., 1975; Japanese translation published by Brain Book Company, Tokyo, 1977.)
    2.       Clive L. Dym, Introduction to the Theory of Shells, Pergamon Press, Oxford, 1974. (Updated edition, Hemisphere Publishing, New York, 1990.)
    3.       Clive L. Dym, Stability Theory and Its Applications to Structural Mechanics, Noordhoff International Publishing Company, Leyden, The Netherlands, 1974. (Republished by Dover Publications, New York, 2002.)
    4.       Irving H. Shames and Clive L. Dym, Energy and Finite Element Methods in Structural Mechanics, Hemisphere Publishing, New York, 1985. (SI Units Edition, Taylor & Francis, New York, 1991.)
    5.       Clive L. Dym and Raymond E. Levitt, Knowledge-Based Systems in Engineering, McGraw-Hill Book Company, New York, 1991.
    6.       Clive L. Dym, Engineering Design: A Synthesis of Views, Cambridge University Press, New York, 1994.
    7.       Clive L. Dym, Structural Modeling and Analysis, Cambridge University Press, New York, 1997.
    8.       Clive L. Dym and Patrick Little, Engineering Design: A Project-Based Introduction, John Wiley & Sons, New York, 1999 (1st Edition), 2004 (2nd Edition), 2009 (3rd Edition, with E. J. Orwin and R. E. Spjut). Spanish translation published by Limusa Wiley, Balderas, Mexico, 2002; Korean translation published by Info-Tech Corea, Seoul, South Korea, 2008; Portuguese translation published by Artmed Editora, S.A., Porto Alegre RS, Brazil, 2010.
    9.       Philip D. Cha, James J. Rosenberg, and Clive L. Dym, Fundamentals of Modeling and Analyzing Engineering Systems, Cambridge University Press, New York, 2000.
    10.    Clive L. Dym, Principles of Mathematical Modeling, 2nd Edition, Elsevier Academic Press, New York, 2004. (First edition, 1980, co-authored by Elizabeth S. Ivey.)
    11.    Jennifer Stroud Rossman and Clive L. Dym, An Introduction to Engineering Mechanics: A Continuum Approach, CRC Press, Boca Raton, Florida, 2008.
    12.    Clive L. Dym and Harry E. Williams, Analytical Estimates of Structural Behavior, CRC Press, Boca Raton, Florida, in press, for 2012 publication.
    13.    Clive L. Dym and David C. Brown, Engineering Design: Representation and Reasoning, Cambridge University Press, New York, in press for 2012 publication.
     
    EDITED BOOKS, PROCEEDINGS and SPECIAL ISSUES
    1.       Arturs Kalnins and Clive L. Dym, Editors, Vibration: Beams, Plates and Shells, Dowden, Hutchinson and Ross, Stroudsburg, Pennsylvania, 1976.
    2.       Clive L. Dym, Editor, Applications of Knowledge-Based Systems to Engineering Analysis and Design, American Society of Mechanical Engineers, New York, 1985.
    3.       Clive L. Dym, Editor, Computing Futures in Engineering Design, Proceedings of Mudd Design Workshop I, Harvey Mudd College, Claremont, California, 2–3 May 1997. See also the Special Issue: Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 12 (1), January 1998.
    4.       Clive L. Dym, Editor, Designing Design Education for the 21st Century, Proceedings of Mudd Design Workshop II, Harvey Mudd College, Claremont, California, 19–21 May 1999. See also the Special Issue, co-Guest Editor Sheri D. Sheppard: International Journal of Engineering Education, 17 (4, 5), 2001.
    5.       Clive L. Dym and Langdon Winner, Editors, Social Dimensions of Engineering Design, Proceedings of Mudd Design Workshop III, Harvey Mudd College, Claremont, California, 17–19 May 2001. See also the Special Issue: International Journal of Engineering Education, 19 (1), 2003.
    6.       Clive L. Dym, Editor, Designing Engineering Education, Proceedings (CD) of Mudd Design Workshop IV, Harvey Mudd College, Claremont, California, 10–12 July 2003. See also the Special Issue: International Journal of Engineering Education, 20 (1), 2004.
    7.       Clive L. Dym, Editor, Learning and Engineering Design, Proceedings (CD) of Mudd Design Workshop V, Harvey Mudd College, Claremont, California, 19–21 May 2005. See also the Special Issue: International Journal of Engineering Education, 22 (3), 2006.
    8.       Philip E. Doepker and Clive L. Dym, Guest Editors, Design Engineering Education, Special Issue, Journal of Mechanical Design, 129 (7), July 2007.
    9.       Clive L. Dym, Editor, Design and Engineering Education in a Flat World, Proceedings (CD) of Mudd Design Workshop VI, Harvey Mudd College, Claremont, California, 23–25 May 2007. See also the Special Issue: International Journal of Engineering Education, 24 (2), 2008.
    10.    Clive L. Dym, Editor, Sustaining Sustainable Design, Preliminary Proceedings (CD) of Mudd Design Workshop VII, Harvey Mudd College, Claremont, California, 29–31 May 2009. See also the Special Issue: co-Guest-Editor Janis P. Terpenny: International Journal of Engineering Education, 26 (2), 2010.
    11.    Daniel D. Frey, William P. Birmingham and Clive L. Dym, Guest Editors, Design Pedagogy: Representations and Processes, Special Issue, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 24 (3), Summer 2010.
    12.    Clive L. Dym, Editor, Design Education: Innovation and Entrepreneurship, Preliminary Proceedings (CD) of Mudd Design Workshop VIII, Harvey Mudd College, Claremont, California, 26–28 May 2011. See also the Special Issue: International Journal of Engineering Education, 28 (2), 2012.
     
    REFEREED JOURNAL ARTICLES
    1.       J. M. Klosner and C. L. Dym, “Axisymmetric Plane-Strain Dynamic Response of a Thick Orthotropic Shell,” Journal of Acoustical Society of America, 39 (1), 1-7, January 1966.
    2.       C. L. Dym and M. L. Rasmussen, “On a Perturbation Problem in Structural Dynamics,” International Journal of Non-Linear Mechanics, 3 (3), 215-225, June 1968.
    3.       C. L. Dym and N. J. Hoff, “Perturbation Solutions for the Buckling Problems of Axially Compressed Thin Cylindrical Shells of Infinite or Finite Length,” Journal of Applied Mechanics, 35 (4), 754-762, December 1968.
    4.       C. L. Dym, “Dynamics of Anisotropic Elastic Solids,” Journal of the Engineering Mechanics Division ASCE, 94 (EM6), 1591-1595, December 1968.
    5.       C. L. Dym, “On the Dynamics of a Shell in a Testing Machine,” International Journal of Non-Linear Mechanics, 4 (2), 17-22, March 1969.
    6.       C. L. Dym and H. Reismann, “On the Time-Dependent Heat Conduction and Thermoelastic Problems,” Quarterly of Applied Mathematics, 27 (1), 121-124, April 1969.
    7.       C. L. Dym, “Vibrations of Pressurized Orthotropic Cylindrical Membranes,” AIAA Journal, 8 (4), 693-699, April 1970.
    8.       D. B. Taulbee, F. A. Cozzarelli and C. L. Dym, “Similarity Solutions to Some Nonlinear Impact Problems,” International Journal of Non-Linear Mechanics, 6 (1), 27- 43, February 1971.
    9.       R. E. Schwartz and C. L. Dym, “An Integer Maximization Problem,” Operations Research, 19 (2), 548-550, March-April 1971.
    10.    C. L. Dym and R. D. Turner, “Some Remarks on Velocity-Aided Kalman Filtering,” IEEE Transactions on Aerospace and Electronic Systems, AES-7 (3), 434-437, May 1971.
    11.    C. L. Dym, “Vibrations of Pressurized Orthotropic Shells,” AIAA Journal, 9 (6), 1201-1203, May 1971.
    12.    C. G. Culver, C. L. Dym and D. K. Brogan, “Bending Behavior of Cylindrical Web Panels,” Journal of the Structural Division ASCE, 98 (ST10), 2291-2308, October 1972.
    13.    C. G. Culver and C. L. Dym, “Elastic Buckling of Stiffened Plates,” Journal of the Structural Division ASCE, 98 (ST11), 2641-2645, November 1972.
    14.    C. L. Dym, “Buckling and Postbuckling Behavior of Steep Compressible Arches,” International Journal of Solids and Structures, 9 (1), 129-140, January 1973.
    15.    C. G. Culver, C. L. Dym and T. Uddin, “Web Slenderness Requirements for Curved Girders,” Journal of the Structural Division ASCE, 99 (ST3), 417-430, March 1973.
    16.    C. L. Dym, “Bifurcation Analyses for Shallow Arches,” Journal of the Engineering Mechanics Division ASCE, 99 (EM2), 287-301, April 1973.
    17.    N. Mariani, J. D. Mozer, C. L. Dym and C. G. Culver, “Transverse Stiffener Requirements for Curved Webs,” Journal of the Structural Division ASCE, 99 (ST4), 757- 771, April 1973.
    18.    C. L. Dym, “On the Buckling of Cylinders in Axial Compression,” Journal of Applied Mechanics, 40 (2), 565-568, June 1973.
    19.    C. L. Dym, “Some New Results for the Vibrations of Circular Cylinders,” Journal of Sound and Vibration, 29 (2), 189-205, 22 July 1973.
    20.    C. L. Dym, “On Rapidly Convergent Solutions to Acoustics Problems with Time-Dependent Boundary Conditions,” Journal of Sound and Vibration, 30 (1), 130-134, 8 September 1973.
    21.    B. R. El-Zaouk and C. L. Dym, “Nonlinear Vibrations of Orthotropic Doubly-Curved Shallow Shells,” Journal of Sound and Vibration, 31 (1), 89-113, 8 November 1973.
    22.    C. L. Dym, “On Some Recent Approaches to Structural Optimization,” Journal of Sound and Vibration, 32 (1), 49-70, 8 January 1974.
    23.    C. L. Dym, “A More Direct Derivation of the Radiation Resistance of a Panel,” Journal of Sound and Vibration, 32 (2), 279-282, 22 January 1974.
    24.    C. L. Dym, “On Approximations of the Buckling Stresses of Axially Compressed Cylinders,” Journal of Applied Mechanics, 41 (1), 163-167, March 1974.
    25.    W. L. Cooley and C. L. Dym, “The Future of Private Engineering Education,” Engineering Issues ASCE, 100 (EI2), 97-103, April 1974.
    26.    C. L. Dym, “Effects of Prestress on the Acoustic Behavior of Panels,” Journal of the Acoustical Society of America, 55 (5), 1018-1021, May 1974. See also C. L. Dym, “Comments on ‘Effects of Prestress on the Acoustic Behavior of Panels,’” Journal of the Acoustical Society of America, 57 (6), 1543-1544, June 1975.
    27.    C. L. Dym and M. A. Lang, “Transmission of Sound Through Sandwich Panels,” Journal of the Acoustical Society of America, 56 (5), 1523-1532, November 1974.
    28.    M. A. Lang and C. L. Dym, “Optimal Acoustic Design of Sandwich Panels,” Journal of the Acousical Society of America, 57 (6), 1481-1487, June 1975.
    29.    C. L. Dym, C. S. Ventres and M. A. Lang, “Transmission of Sound Through Sandwich Panels: A Reconsideration,” Journal of the Acoustical Society of America, 59 (2), 364-367, February 1976.
    30.    C. L. Dym, “Variational Methods of Analysis,” Shock and Vibration Digest, 8 (7), 27-31, July 1976.
    31.    E. E. Ungar, C. L. Dym and R. W. White, “Prediction and Control of Vibrations in Buildings,” Shock and Vibration Digest, 8 (9), 13-24, September 1976.
    32.    T. G. Gutowski and C. L. Dym, “Propagation of Ground Vibration: A Review,” Journal of Sound and Vibration, 49 (2), 179-193, November 1976.
    33.    C. L. Dym, “Sources of Industrial Impact/Impulsive Noise,” Noise Control Engineering, 8 (2), 81–87, March-April 1977.
    34.    C. L. Dym, “Buckling of Supported Arches Under Three Pressure Distributions,” Journal of Applied Mechanics, 44 (4), 764-766, December 1977.
    35.    T. G. Gutowski, L. E. Wittig and C. L. Dym, “Some Aspects of the Ground Vibration Problem,” Noise Control Engineering, 10 (3), 94-100, May-June 1978.
    36.    P. W. Smith, Jr., and C. L. Dym, “Input and Transfer Admittances of Thick Plates Driven by a Uniform Line Moment,” Journal of Sound and Vibration, 60 (3), 441-447, 8 October 1978.
    37.    A. B. Perlman and C. L. Dym, “A Note on the Lyapunov Stability Analysis of a Linear Railway Wheelset,” Vehicle Systems Dynamics, 9 (3), 61-68, March 1980.
    38.    C. L. Dym, “On the Vibration of Thin Circular Rings,” Journal of Sound and Vibration, 70 (4), 585-588, 22 June 1980. 
    39.    C. L. Dym, “Stiffening of Bent Beams Due to Partial and Complete End Restraint,” International Journal of Non-Linear Mechanics, 16 (1), 39-45, January 1981.
    40.    H. A. Hyman, A. Ballantyne, H. W. Friedman, D. A. Reilly, R. C. Southworth and C. L. Dym, “Intense Pulsed Plasma X-ray Sources for Lithography: Mask Damage Effects,” Journal of Vacuum Science and Technology, 21 (4), 1012-1016, November-December 1982.
    41.    C. L. Dym, “Analysis and Modeling in Mechanics: An Informal View,” Computers and Structures, 16 (1–4), 101-107, January 1983.
    42.    C. L. Dym and D. C. Lang, “Transmission Loss of Damped Asymmetric Sandwich Panels with Orthotropic Cores,” Journal of Sound and Vibration, 88 (3), 299-319, 8 June 1983. 
    43.    C. L. Dym, “Expert Systems: New Approaches to Computer-Aided Engineering,” Engineering with Computers, 1 (1), 9-25, April 1985. See also Proceedings of the 25th AIAA-ASME-ASCE-AHS Structures, Structural Dynamics and Materials Conference, pp. 99-115, May 1984.
    44.    S. Mittal and C. L. Dym, “Knowledge Acquisition from Multiple Experts,” AI Magazine, 6 (2), 32-36, Summer 1985. See also Proceedings of the IEEE Workshop on Principles of Knowledge-Based Systems, pp. 75-81, December 1984.
    45.    Ballantyne, H. A. Hyman, C. L. Dym and R. Southworth, “Response of Lithographic Mask Structures to Repetitively Pulsed X-rays: Thermal Stress Analysis,” Journal of Applied Physics, 58 (12), 4714-4725, 15 December 1985.
    46.    C. L. Dym and A. Ballantyne, “Response of Lithographic Mask Structures to Repetitively Pulsed X-rays: Dynamic Response,” Journal of Applied Physics, 58 (12), 4726-4729, 15 December 1985.
    47.    S. E. Makris, C. L. Dym and J. M. Smith, “Transmission Loss Optimization in Acoustic Sandwich Panels,” Journal of the Acoustical Society of America, 79 (6), 1833-1843, June 1986. 
    48.    S. Mittal, C. L. Dym and M. Morjaria, “PRIDE: An Expert System for the Design of Paper Handling Systems,” Computer, 19 (7), 102-114, July 1986. See also C. L. Dym (Editor), Applications of Knowledge-Based Systems to Engineering Analysis and Design, American Society of Mechanical Engineers, pp. 99-115, November 1985.
    49.    J. R. Dixon and C. L. Dym, “Artificial Intelligence and Geometric Reasoning in Manufacturing Technology,” Applied Mechanics Reviews, 39 (9), 1325-1330, September 1986.
    50.    C. L. Dym, “Issues in the Design and Implementation of Expert Systems,” Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 1 (1), 37-46, December 1987.
    51.    C. L. Dym, R. P. Henchey, E. A. Delis and S. Gonick, “A Knowledge-Based System for Automated Architectural Code-Checking,” Computer-Aided Design, 20 (3), 137-145, April 1988.
    52.    S. E. Makris, J. M. Smith and C. L. Dym, Engineering Optimization, “Multi-Objective Optimization of Acoustic Sandwich Panels,” 13 (4), 147-172, May 1988. 
    53.    S. E. Salata and C. L. Dym, “Representing Strategic Choices in Structural Modeling,” Journal of Computing in Civil Engineering, 5 (4), 354-373, October 1991.
    54.    C. L. Dym and R. E. Levitt, “Toward the Integration of Knowledge for Engineering Modeling and Computation,” Engineering with Computers, 7 (4), 209-224, Fall 1991. 
    55.    R. E. Levitt, Y. Jin and C. L. Dym, “Knowledge-Based Support for Management of Concurrent, Multidisciplinary Design,” Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 5 (2), 77-95, 1991.
    56.    C. L. Dym, “Representation and Problem Solving: The Foundations of Engineering Design,” Planning and Design: Environment and Planning B, 19, 97-105, 1992.
    57.    C. L. Dym, “The Role of Symbolic Representation in Engineering Design Education,” IEEE Transactions on Education, 36 (1), 187-193, February 1993.
    58.    C. L. Dym, S. R. H. Hoole and D. Kurumbalapitiya, “Defining and Representing Knowledge in Electromagnetic Field Computation,” IEEE Transactions on Magnetics, 29 (2), 1935-1938, March 1993.
    59.    C. L. Dym, “Representing Designed Objects: The Languages of Engineering Design,” Archives for Computational Methods in Engineering, 1 (1), 75-108, 1994.
    60.    C. L. Dym, “Teaching Design to Freshmen: Style and Content,” Journal of Engineering Education, 83 (4), 303-310, October 1994.
    61.    C. L. Dym and R. E. Levitt, “On the Evolution of CAE Research,” Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 8 (4), 275-282, Fall 1994.
    62.    C. L. Dym, M. D. Summers, C. T. Demel and C. S. Wong, “DEEP: A Knowledge-Based (Expert) System for Electrical Plat Design,” Computing Systems in Engineering, 6 (6), 497-509, 1995.
    63.    P. D. Cha and C. L. Dym, “Identifying Nodes and Anti-Nodes of an Axially Vibrating Bar with Lumped Mass,” Journal of Sound and Vibration,” 203 (3), 533–535, 12 June 1997.
    64.    C. L. Dym, “Design and Design Centers in Engineering Education,” Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 12 (1), 43–46, January 1998.
    65.    P. D. Cha, C. L. Dym and W. C. Wong, “Identifying Nodes and Anti-Nodes of Complex Structures with Virtual Elements,” Journal of Sound and Vibration,” 211 (1), 249-264, 3 March 1998.
    66.    S. D. Sheppard, R. Jenison, C. L. Dym, et al., “Examples of Freshman Design Education,” International Journal of Engineering Education, 13 (4), 248–261, 1997.
    67.    C. L. Dym, “Learning Engineering: Design, Languages, and Experiences,” Journal of Engineering Education, 88 (2), 145-148, April 1999.
    68.    D. M. Mikes, B. A. Cha, C. L. Dym, J. Baumgaertner, A. G. Hartzog, A. D. Tacey and M. R. Calabria, “Bioelectrical Impedance Analysis Revisited,” Lymphology, 32 (4), 157-165, December 1999.
    69.    C. L. Dym and P. Brey, “Languages of Engineering Design: Empirical Constructs for Representing Objects and Articulating Processes,” Research in Philosophy and Technology, 20, 119-148, 2000.
    70.    C. L. Dym, S. D. Sheppard and J. W. Wesner, “A Report on Mudd Design Workshop II: Designing Design Education for the 21st Century,” Journal of Engineering Education, 90 (3), 291-294, July 2001.
    71.    C. L. Dym, W. H. Wood and M. J. Scott, “Rank Ordering Engineering Designs: Pairwise Comparison Charts and Borda Counts,” Research in Engineering Design, 13, 236–242, 2002.
    72.    C. L. Dym, J. W. Wesner and L. Winner, “A Report on Mudd Design Workshop III: Social Dimensions of Engineering Design,” Journal of Engineering Education, 92 (1), 105–107, January 2003.
    73.    C. L. Dym, “Social Dimensions of Engineering Design . . . An Engineer’s Perspective,” International Journal of Engineering Education, 19 (1), 3–5, 2003.
    74.    C. L. Dym and H. E. Williams, “On the Analysis of Small Displacements of Truss Joints,” International Journal of Mechanical Engineering Education, 31 (2), 132–140, 2003.
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    83.    C. L. Dym and H. E. Williams, “Estimating Fundamental Frequencies of Tall Buildings,” Journal of Structural Engineering, 133 (10), 1479–1483, October 2007.
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  • Design Heuristics in Innovative Products

    Seda Yilmaz, Colleen Seifert, Shanna R. Daly and Richard Gonzalez
    J. Mech. Des 138(7), 071102; doi: 10.1115/1.4032219
    Current design theory lacks a systematic method to identify what designers know that helps them to create innovative products. In the early stages of idea generation, designers may find novel ideas come readily to mind, or may become fixated on their own or existing products. This may limit the ability to consider more, and more varied candidate concepts that may potentially lead to innovation. To aid in idea generation, we sought to identify “design heuristics,” or “rules of thumb,” evident in award-winning designs. In this paper, we demonstrate a content analysis method for discovering heuristics in the designs of innovative products. Our method depends on comparison to a baseline of existing products so that the innovative change can be readily identified. Through an analysis of key features and functional elements in the designs of over 400 award-winning products, forty heuristic principles were extracted. These Design Heuristics are outlined according to their perceived role in changing an existing product concept into a novel design, and examples of other products using the heuristics are provided. To demonstrate the ease of use of these Design Heuristics, we examined outcomes from a classroom study, and found that concepts created using Design Heuristics were rated as more creative and varied. The analysis of changes from existing to innovative products can provide evidence of useful heuristic principles to apply in creating new designs.
     

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  • Synthesizing Functional Mechanisms From a Link Soup

    Pouya Tavousi, Kazem Kazerounian and Horea Ilies
    J. Mech. Des 138(6); doi: 10.1115/1.4033394
    The synthesis of functional molecular mechanisms is constrained by the notorious difficulties in fabricating nano-links of prescribed shapes and sizes. Thus, the classical mechanism synthesis methods, which assume the ability to manufacture any designed links, cannot provide a systematic process for designing molecular mechanisms. We propose a new approach to build functional mechanisms with prescribed mobility by only using elements from a predefined "link soup". The resulting synthesis procedure is the first of its kind that is capable of systematically synthesizing functional linkages with prescribed mobility constructed from a soup of primitive entities. Furthermore, the proposed systematic approach outputs the ATLAS of candidate mechanisms, which can be further processed for downstream applications. Although the scope of this technique is rather general, its immediate application is the design of molecular machines assembled from nano-links that either exist in nature or can be fabricated.
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