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Authors: Changming Yang, Xiaoping DuJ. Mech. Des. 136(10), 101405 (2014) (8 pages) Paper No: MD-13-1534; doi: 10.1115/1.4028016
Authors: Katherine Fu, DIana Moreno, Maria Yang, and Kristen L. Wood
J. Mech. Des. 136(11), 111102 (2014) (18 pages)doi: 10.1115/1.4028289
Authors: Mark Fuge, Bud Peters and Alice Agogino
Designers use specific methods to discern people’s needs and how to best create products or services that meet those needs. Choosing precisely the right method for a given problem is extremely difficult: it requires a deep understanding of the nature of the problem, knowledge of the vast array of design methods, and years of experience. This paper demonstrates that by collecting expert experience in the form of case studies, machine learning algorithms can help new designers pick better design methods and understand how methods are related to one another. Specifically, we show that looking at which methods designers use together can be more informative than just looking at the content of the method itself. In addition, you can use counts of which methods are used together to automatically cluster methods into groups that agree with human ratings; this means that you can study many more methods than could be done manually.