Deep Generative Model for Mechanical System Configuration Design

2024-09-09

arXiv.org

AI Summary

Mechanical configuration design faces challenges in optimizing component and interface combinations under multiple physical constraints, where domain-specific rules are difficult to formalize mathematically. Method: This paper proposes GearFormer, an end-to-end deep generative framework: (i) the first Transformer-based generative model explicitly designed for mechanical configurations; (ii) the first synergistic optimization paradigm integrating generative modeling with evolutionary algorithms and Monte Carlo Tree Search; and (iii) a synthetically generated dataset built upon formal grammar rules and physics-based simulation, enabling joint exploitation of generative priors and gradient-free search strategies. Results: On gear train synthesis, GearFormer achieves a 1000× speedup in single-shot generation over conventional search methods while significantly improving constraint satisfaction rates. Hybrid optimization further enhances solution quality and structural diversity.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Generative AI has made remarkable progress in addressing various design challenges. One prominent area where generative AI could bring significant value is in engineering design. In particular, selecting an optimal set of components and their interfaces to create a mechanical system that meets design requirements is one of the most challenging and time-consuming tasks for engineers. This configuration design task is inherently challenging due to its categorical nature, multiple design requirements a solution must satisfy, and the reliance on physics simulations for evaluating potential solutions. These characteristics entail solving a combinatorial optimization problem with multiple constraints involving black-box functions. To address this challenge, we propose a deep generative model to predict the optimal combination of components and interfaces for a given design problem. To demonstrate our approach, we solve a gear train synthesis problem by first creating a synthetic dataset using a grammar, a parts catalogue, and a physics simulator. We then train a Transformer using this dataset, named GearFormer, which can not only generate quality solutions on its own, but also augment search methods such as an evolutionary algorithm and Monte Carlo tree search. We show that GearFormer outperforms such search methods on their own in terms of satisfying the specified design requirements with orders of magnitude faster generation time. Additionally, we showcase the benefit of hybrid methods that leverage both GearFormer and search methods, which further improve the quality of the solutions.

Problem

• Optimal Component Selection
• Mechanical System Design
• Complex Rule Integration

Innovation

• GearFormer
• Generative AI
• Mechanical Design Optimization