A novel deep generative model based on imaginal thinking for automating design |
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| Affiliation: | 1. State Key Laboratory of Mechanical System and Vibration, Shanghai 200240, China;2. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;1. Department of Mechanical Engineering, KU Leuven, Leuven 3000, Belgium;2. Materialise NV, Leuven 3001, Belgium;1. Department of Electrical and Computer Engineering University of Kentucky, Lexington, KY, United States of America;2. Department of Mechanical Engineering, University of Kentucky, Lexington, KY, United States of America;3. Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH, United States of America;1. Department of Mechanical Engineering, Osaka University, Osaka, Japan;2. M4 Sciences Corporation, Lafayette, IN, USA;3. R&D Material and Technology Development, Seco Tools AB, Fagersta, Sweden;4. Center for Materials Processing and Tribology, Purdue University, West Lafayette, IN, USA |
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| Abstract: | Automating design faces a thorny problem: insight modeling based on knowledge and experience. In particular, it is difficult for artificial intelligence to perform incomplete conditional reasoning. The deep generative model (DGM) is an emerging approach of machine learning, which typically uses deep networks to learn from various data sets and synthesize new designs. This paper proposes a novel DGM based on imaginal thinking to realize the creative leap from the invisible functional domain to the concrete physical domain. An experiment is conducted to verify the effectiveness of the proposed model in designing wheels for mobile robots in granular media. |
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