2School of Computing Science, Simon Fraser University, Burnaby, BC, Canada
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.
A showcase of our approach to mechanical system configuration design
To demonstrate our approach's effectiveness, we applied our technique to gear train design synthesis.
Below are examples of gear configurations automatically generated by our AI model based on specific
design requirements, showcasing the model's ability to create diverse, physically valid mechanical
systems.
GearFormer Architecture
A Transformer-based model for mechanical system configuration design
Deep Learning Core
Transformer architecture optimized for mechanical design with adaptive loss weighting
between sequence prediction and physical constraints
Physics-Aware Design
Incorporates mechanical constraints and physical requirements through specialized loss
functions and Dymos-based simulation
Domain Specific Language
Incorporates a formal grammar and lexicon for mechanical systems, enabling structured
generation of physically valid gear configurations
Hybrid Transformer + MCTS
Combining the power of Monte Carlo Tree Search with GearFormer
Strategic Exploration
MCTS explores the vast design space by focusing on the most promising
initial component choices
AI-Powered Completion
GearFormer leverages learned patterns to complete configurations with high success rates
Superior Performance
The hybrid approach achieves better results than either method alone, with significantly
fewer iterations
Demo - Generation
Interactive UI for rapid mechanical system design exploration
Intuitive interfaces can be designed on top of such systems to specify requirements and explore
diverse gear configurations.
The AI-powered system generates physically valid designs in
real-time,
allowing quick iteration and exploration of the design space. This dramatically accelerates the
mechanical system design process
while ensuring all generated solutions meet the specified constraints.
Demo - Copilot
AI-powered design assistance with GearFormer
GearFormer's transformer model powers an intelligent copilot that assists designers during the
configuration process.
The system provides smart
autocompletion suggestions,
predicting optimal component combinations that satisfy both the partial design and physical
constraints. This creates a fluid,
collaborative design experience between human expertise and AI assistance.
