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Integrating Deep Learning into CAD/CAE System: Generative Design and Evaluation of 3D Conceptual Wheel

arXiv 2006.02138 ↗First posted 25 May 2020cs.GRcited by 25

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Abstract (arXiv)

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.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 May 2020.

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Citing papers per year: 2021: 2; 2022: 4; 2023: 4; 2024: 7; 2025: 3; 2026: 5.

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  1. 2021JoinABLe: Learning Bottom-up Assembly of Parametric CAD Jointscited by 78
  2. 2023Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generationcited by 52
  3. 2021CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesiscited by 24
  4. 2023Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Designcited by 23
  5. 2023Deep Learning in Deterministic Computational Mechanicscited by 21
  6. 2022Design Target Achievement Index: A Differentiable Metric to Enhance Deep Generative Models in Multi-Objective Inverse Designcited by 6
  7. 2022Deep Learning-Based Inverse Design for Engineering Systems: Multidisciplinary Design Optimization of Automotive Brakescited by 5
  8. 2022Wheel Impact Test by Deep Learning: Prediction of Location and Magnitude of Maximum Stresscited by 5
  9. 2024NITO: Neural Implicit Fields for Resolution-free Topology Optimizationcited by 3
  10. 2024Bayesian Mesh Optimization for Graph Neural Networks to Enhance Engineering Performance Predictioncited by 3
  11. 2025DeepWheel: Generating a 3D Synthetic Wheel Dataset for Design and Performance Evaluationcited by 3
  12. 2024Deep Generative Design for Mass Productioncited by 2
13 more
  1. 2025Three-dimensional Deep Shape Optimization with a Limited Datasetcited by 2
  2. 2026BenDFM: A taxonomy and synthetic CAD dataset for manufacturability assessment in sheet metal bendingcited by 2
  3. 2022Social Computational Design Method for Generating Product Shapes with GAN and Transformer Modelscited by 1
  4. 2023Weighted Unsupervised Domain Adaptation Considering Geometry Features and Engineering Performance of 3D Design Datacited by 1
  5. 2024McGAN: Generating Manufacturable Designs by Embedding Manufacturing Rules into Conditional Generative Adversarial Networkcited by 1
  6. 2025Generative Parametric Design (GPD): A framework for real-time geometry generation and on-the-fly multiparametric approximationcited by 1
  7. 2026Zero-shot rib design: merging training-free generative prior with topology optimizationcited by 1
  8. 2024Layout2Rendering: AI-aided Greenspace designcited by 0
  9. 2024Vehicle Suspension Recommendation System: Multi-Fidelity Neural Network-based Mechanism Design Optimizationcited by 0
  10. 2024Deep Concept Identification for Generative Designcited by 0
  11. 2026RePaint-Enhanced Conditional Diffusion Model for Parametric Engineering Designs under Performance and Parameter Constraintscited by 0
  12. 2026Enhancing Creativity in 3D Generative Design via a TRIZ-Inspired Text-to-CAD Frameworkcited by 0
  13. 2026PhysicsBench: A Unified Leaderboard for Generative and Predictive Models in Engineering Design and Simulationcited by 0