Concept clustering is an important element of the product development process. The process of reviewing multiple concepts provides a means of communicating concepts developed by individual team members and by the team as a whole. Clustering, however, can also require arduous iterations and the resulting clusters may not always be useful to the team. In this paper, we present a machine learning approach on natural language descriptions of concepts that enables an automatic means of clustering. Using data from over 1,000 concepts generated by student teams in a graduate new product development class, we provide a comparison between the concept clustering performed manually by the student teams and the work automated by a machine learning algorithm. The goal of our machine learning tool is to support design teams in identifying possible areas of “over-clustering” and/or “under-clustering” in order to enhance divergent concept generation processes.
- Design Engineering Division
- Computers and Information in Engineering Division
Deep Learning for Design in Concept Clustering
Zhang, C, Kwon, YP, Kramer, J, Kim, E, & Agogino, AM. "Deep Learning for Design in Concept Clustering." Proceedings of the ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 1: 37th Computers and Information in Engineering Conference. Cleveland, Ohio, USA. August 6–9, 2017. V001T02A019. ASME. https://doi.org/10.1115/DETC2017-68352
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