Official ASME Group

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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    Darren J. HartlEdgar GalvanRichard J. Malak and Jeffrey W. Baur
    J. Mech. Des 138(3), 031402; doi: 10.1115/1.4032268 
    ​This work is the first demonstration of the use of a parameterized approach to design optimization on a complex engineering problem. Parameterized optimization solves a family of optimization problems as a function of exogenous parameters. When applied to a subsystem of interest, it results in general knowledge about the capabilities of the subsystem rather than a restrictive point solution. The motivation is that it often is necessary to advance the development of a subsystem independent of system-level specifics. This is true during initial research and development efforts or in sensitive military and competitive industrial design environments in which compartmentalization of information is common and necessary. It also is important in systems development projects when the need for concurrency often requires subsystem designers to make progress in the absence of full information about other interfacing subsystems. We solve this specialized design problem using the predictive parameterized Pareto genetic algorithm (P3GA). The approach is demonstrated for the multifunctional design of a structurally-integrated liquid metal circuit intended to provide integrated cooling functionality.  A family of optimal design solutions associated with values of external parameters (bounded real numbers) is computed efficiently using P3GA. The demonstration employs both high- and low-fidelity multi-physical engineering models seamlessly and results in general knowledge about the subsystem as a function of parameters associated with other interfacing subsystems.
    For the full paper please see ASME's Digital Collection

    Newsha KhaniJames Humann and Yan Jin
    J. Mech. Des 138(4), 041101; doi: 10.1115/1.4032265 
    ​Dealing with unforeseeable changing situations, often seen in exploratory and hazardous task domains, requires engineered complex systems that can feature flexibility to fulfill multiple resolutions over long lifespans, robustness to deal with environmental changes and resilience to sustain system damage. The current top-down engineering design approach has its limitations in cases where it is impossible to fully consider and predict the true operational uncertainties because the hidden interdependencies among the system components can lead to unforeseeable interactions at operation time. The challenge for engineering design researchers and practitioners is how to devise new ways to design such adaptive systems. Nature embodies certain qualities such as evolution, cellular organization, and self-organizing behavior, which seem to overcome the deficiencies of the traditional top-down engineering design process. Taking advantage of the flexibility of multi-agent systems, we proposed a self-organizing systems approach, in which mechanical cells or agents organize themselves as the environment and tasks change based on a set of predefined rules. This study is positioned at the interface between the science and engineering of complex systems by taking the “by emergence” approach to achieve desired functions of cellular self-organizing systems.

    Through the case studies we investigated the impact of social rule based social structuring, measured by social rules adoption rate and the size of agent population, on the system performance in the face of increasing task complexity. The results have shed interesting insights including: the behavior of self-organizing systems becomes more chaotic when tasks are more complex; stronger social structuring is effective for a smaller number of agents and weaker social structuring is more effective for a larger number of agents; and there can be a “singular” number of agents where social structuring is neither effective nor efficient.  In conclusion, self-organization has profound implications in dealing with task complexity and can be used intentionally as a tool in the design of adaptive complex systems. The balance of task complexity, the number of agents, and social structuring is the key. Understanding self-organization, minimizing harmful effects, and promoting positive effects will become essential in future engineering design of complex systems.
    For the full paper see ASME's Digital Collection.

    Soheil ArastehfarYing Liu and Wen Feng Lu
    J. Mech. Des 138(3), 031103 (Feb 01, 2016); doi: 10.1115/1.4032396

    ​Digital prototype (DP), as a form of communication media, allows designers to communicate design concepts to users by rendering the physical characteristics, e.g., size, colour, and texture. One important aspect is how well users can estimate the values of the physical characteristics of design concepts through interactions with DPs. Better estimates can lead to better perceptions of the designed attributes closely associated with the physical characteristics, and hence, useful user feedback about design concepts. The correctness of the estimates depends on two crucial factors: the ability of DPs to render physical characteristics and the way DPs are used to communicate physical characteristics in a particular environment and via different input/output devices. To date, little attention has been paid to the latter. Hence, it is important to identify an effective way of using DPs via the effectiveness assessment of various possibilities. This paper introduces a methodology for evaluating the effectiveness of communicating physical characteristics to users using DPs. During user interactions with DPs, the methodology collects user estimates of various physical characteristics and assesses the estimates on three dimensions, i.e., degree of correctness, time to make an estimate and handling of different values. The assessments are then evaluated by statistical analysis to reveal the effectiveness of the way of engaging DPs in helping users correctly and quickly estimate the values. The evaluated effectiveness reflects how successful the way of using a DP is, and also helps to suggest a better approach.  

    Additive manufacturing (AM) techniques provide designers with greater freedom in creating customized products with complex shapes. When major design changes are made to a part, undesirable high cost increments may be incurred due to AM process setting adjustments, challenging designers to explore AM-enabled design freedom while controlling costs at the same time. In this research, we introduce the concept of a variable product platform and its associated AM process setting platform, based on which the design and process setting adjustments can be restricted within a bounded feasible space in order to limit cost increments. Fuzzy Time-Driven Activity-Based Costing (FTDABC) approach is introduced to predict AM production costs based on process settings. The process setting adjustment’s feasible space boundary is identified by solving a multiobjective optimization problem. Design parameter limitations are computed in a Mamdani-type expert system and then used as constraints in the design optimization to maximize customer perceived utility. Case studies on designing an R/C racing car family illustrate the proposed methodology and demonstrate that the optimized additive manufactured variable platforms can improve product performances at lower costs than conventional consistent platform based design.
    For the complete article please see ASME's Digital Collection.