  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.
