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Integrating deep learning into CAD/CAE system: generative design and evaluation of 3D conceptual wheel
Structural and Multidisciplinary Optimization ( IF 3.9 ) Pub Date : 2021-06-14 , DOI: 10.1007/s00158-021-02953-9
Soyoung Yoo , Sunghee Lee , Seongsin Kim , Kwang Hyeon Hwang , Jong Ho Park , Namwoo Kang

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.



中文翻译:

将深度学习融入 CAD/CAE 系统:3D 概念轮的生成式设计与评估

将人工智能(AI)融入计算机辅助设计(CAD)和计算机辅助工程(CAE)的工程设计研究正在积极开展。本研究在概念设计阶段提出了一个基于深度学习的 CAD/CAE 框架,该框架可自动生成 3D CAD 设计并评估其工程性能。所提出的框架包括七个阶段:(1) 2D 生成设计,(2) 降维,(3) 潜在空间中的实验设计,(4) CAD 自动化,(5) CAE 自动化,(6) 迁移学习,以及 ( 7) 可视化和分析。拟议的框架通过车轮设计案例研究进行了证明,并表明人工智能可以实际纳入最终用途产品设计项目。

更新日期:2021-06-14
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