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

arXiv: https://arxiv.org/abs/2006.02138 · HTML: https://mlsanity.com/paper/2006.02138/

## Key facts

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

## Built on by (most cited first, 25 of 25)

- [JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints](https://mlsanity.com/paper/2111.12772/) (2021)
- [Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation](https://mlsanity.com/paper/2305.18470/) (2023)
- [CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesis](https://mlsanity.com/paper/2103.06242/) (2021)
- [Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design](https://mlsanity.com/paper/2302.02913/) (2023)
- [Deep Learning in Deterministic Computational Mechanics](https://mlsanity.com/paper/2309.15421/) (2023)
- [Design Target Achievement Index: A Differentiable Metric to Enhance Deep Generative Models in Multi-Objective Inverse Design](https://mlsanity.com/paper/2205.03005/) (2022)
- [Deep Learning-Based Inverse Design for Engineering Systems: Multidisciplinary Design Optimization of Automotive Brakes](https://mlsanity.com/paper/2202.13309/) (2022)
- [Wheel Impact Test by Deep Learning: Prediction of Location and Magnitude of Maximum Stress](https://mlsanity.com/paper/2210.01126/) (2022)
- [NITO: Neural Implicit Fields for Resolution-free Topology Optimization](https://mlsanity.com/paper/2402.05073/) (2024)
- [Bayesian Mesh Optimization for Graph Neural Networks to Enhance Engineering Performance Prediction](https://mlsanity.com/paper/2406.01996/) (2024)
- [DeepWheel: Generating a 3D Synthetic Wheel Dataset for Design and Performance Evaluation](https://mlsanity.com/paper/2504.11347/) (2025)
- [Deep Generative Design for Mass Production](https://mlsanity.com/paper/2403.12098/) (2024)
- [Three-dimensional Deep Shape Optimization with a Limited Dataset](https://mlsanity.com/paper/2506.12326/) (2025)
- [BenDFM: A taxonomy and synthetic CAD dataset for manufacturability assessment in sheet metal bending](https://mlsanity.com/paper/2603.13102/) (2026)
- [Social Computational Design Method for Generating Product Shapes with GAN and Transformer Models](https://mlsanity.com/paper/2202.10774/) (2022)
- [Weighted Unsupervised Domain Adaptation Considering Geometry Features and Engineering Performance of 3D Design Data](https://mlsanity.com/paper/2309.04499/) (2023)
- [McGAN: Generating Manufacturable Designs by Embedding Manufacturing Rules into Conditional Generative Adversarial Network](https://mlsanity.com/paper/2407.16943/) (2024)
- [Generative Parametric Design (GPD): A framework for real-time geometry generation and on-the-fly multiparametric approximation](https://mlsanity.com/paper/2512.11748/) (2025)
- [Zero-shot rib design: merging training-free generative prior with topology optimization](https://mlsanity.com/paper/2609.10643/) (2026)
- [Layout2Rendering: AI-aided Greenspace design](https://mlsanity.com/paper/2404.16067/) (2024)
- [Vehicle Suspension Recommendation System: Multi-Fidelity Neural Network-based Mechanism Design Optimization](https://mlsanity.com/paper/2410.03045/) (2024)
- [Deep Concept Identification for Generative Design](https://mlsanity.com/paper/2410.20061/) (2024)
- [RePaint-Enhanced Conditional Diffusion Model for Parametric Engineering Designs under Performance and Parameter Constraints](https://mlsanity.com/paper/2602.00384/) (2026)
- [Enhancing Creativity in 3D Generative Design via a TRIZ-Inspired Text-to-CAD Framework](https://mlsanity.com/paper/2606.21378/) (2026)
- [PhysicsBench: A Unified Leaderboard for Generative and Predictive Models in Engineering Design and Simulation](https://mlsanity.com/paper/2608.24056/) (2026)

## 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.