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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Bibliographic Details
Published in:Structural and multidisciplinary optimization Vol. 64; no. 4; pp. 2725 - 2747
Main Authors: Yoo, Soyoung, Lee, Sunghee, Kim, Seongsin, Hwang, Kwang Hyeon, Park, Jong Ho, Kang, Namwoo
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2021
Springer Nature B.V
Subjects:
Artificial intelligence
Automation
CAD
CAE
Computational Mathematics and Numerical Analysis
Computer aided design
Computer aided engineering
Deep learning
Design engineering
Design of experiments
Engineering
Engineering Design
Industrial Application Paper
Performance evaluation
Product design
Theoretical and Applied Mechanics
Three dimensional models
Deep learning
Topology optimization
Generative design
Artificial intelligence
CAE
CAD
ISSN:1615-147X, 1615-1488
Online Access:Get full text
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Summary: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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ISSN:1615-147X
1615-1488
DOI:10.1007/s00158-021-02953-9