Integrating deep learning into CAD/CAE system: generative design and evaluation of 3D conceptual wheel
Engineering design research integrating artificial intelligence (AI) into computer-aided design (CAD) and computer-aided engineering (CAE) is actively being conducted. This study proposes a deep learning-based CAD/CAE framework in the conceptual design phase that automatically generates 3D CAD desig...
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| Veröffentlicht in: | Structural and multidisciplinary optimization Jg. 64; H. 4; S. 2725 - 2747 |
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| Sprache: | Englisch |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.10.2021
Springer Nature B.V |
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| ISSN: | 1615-147X, 1615-1488 |
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| Abstract | Engineering design research integrating artificial intelligence (AI) into computer-aided design (CAD) and computer-aided engineering (CAE) is actively being conducted. This study proposes a deep learning-based CAD/CAE framework in the conceptual design phase that automatically generates 3D CAD designs and evaluates their engineering performance. The proposed framework comprises seven stages: (1) 2D generative design, (2) dimensionality reduction, (3) design of experiment in latent space, (4) CAD automation, (5) CAE automation, (6) transfer learning, and (7) visualization and analysis. The proposed framework is demonstrated through a road wheel design case study and indicates that AI can be practically incorporated into an end-use product design project. Engineers and industrial designers can jointly review a large number of generated 3D CAD models by using this framework along with the engineering performance results estimated by AI and find conceptual design candidates for the subsequent detailed design stage. |
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| AbstractList | Engineering design research integrating artificial intelligence (AI) into computer-aided design (CAD) and computer-aided engineering (CAE) is actively being conducted. This study proposes a deep learning-based CAD/CAE framework in the conceptual design phase that automatically generates 3D CAD designs and evaluates their engineering performance. The proposed framework comprises seven stages: (1) 2D generative design, (2) dimensionality reduction, (3) design of experiment in latent space, (4) CAD automation, (5) CAE automation, (6) transfer learning, and (7) visualization and analysis. The proposed framework is demonstrated through a road wheel design case study and indicates that AI can be practically incorporated into an end-use product design project. Engineers and industrial designers can jointly review a large number of generated 3D CAD models by using this framework along with the engineering performance results estimated by AI and find conceptual design candidates for the subsequent detailed design stage. |
| Author | Lee, Sunghee Kim, Seongsin Kang, Namwoo Park, Jong Ho Hwang, Kwang Hyeon Yoo, Soyoung |
| Author_xml | – sequence: 1 givenname: Soyoung surname: Yoo fullname: Yoo, Soyoung organization: Department of Mechanical Systems Engineering, Sookmyung Women’s University – sequence: 2 givenname: Sunghee surname: Lee fullname: Lee, Sunghee organization: Department of Mechanical Systems Engineering, Sookmyung Women’s University – sequence: 3 givenname: Seongsin surname: Kim fullname: Kim, Seongsin organization: Department of Mechanical Systems Engineering, Sookmyung Women’s University – sequence: 4 givenname: Kwang Hyeon surname: Hwang fullname: Hwang, Kwang Hyeon organization: Hyundai Motor Company – sequence: 5 givenname: Jong Ho surname: Park fullname: Park, Jong Ho organization: Hyundai Motor Company – sequence: 6 givenname: Namwoo orcidid: 0000-0003-3475-7477 surname: Kang fullname: Kang, Namwoo email: nwkang@kaist.ac.kr organization: The Cho Chun Shik Graduate School of Green Transportation, Korea Advanced Institute of Science and Technology |
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