Data-Driven Dimensional Synthesis of Diverse Planar Four-bar Function Generation Mechanisms via Direct Parameterization

TL;DR

This paper introduces a data-driven neural network approach for the inverse kinematic synthesis of planar four-bar mechanisms, replacing traditional methods with a supervised learning framework that handles diverse linkage types efficiently.

Contribution

It presents a novel combination of synthetic datasets, LSTM networks, and Mixture of Experts architecture for flexible, accurate mechanism synthesis without solving complex equations.

Findings

Accurate mechanism synthesis across various configurations

Defect-free linkages generated by the model

Enables intuitive design for non-experts

Abstract

Dimensional synthesis of planar four-bar mechanisms is a challenging inverse problem in kinematics, requiring the determination of mechanism dimensions from desired motion specifications. We propose a data-driven framework that bypasses traditional equation-solving and optimization by leveraging supervised learning. Our method combines a synthetic dataset, an LSTM-based neural network for handling sequential precision points, and a Mixture of Experts (MoE) architecture tailored to different linkage types. Each expert model is trained on type-specific data and guided by a type-specifying layer, enabling both single-type and multi-type synthesis. A novel simulation metric evaluates prediction quality by comparing desired and generated motions. Experiments show our approach produces accurate, defect-free linkages across various configurations. This enables intuitive and efficient mechanism design, even for non-expert users, and opens new possibilities for scalable and flexible synthesis in kinematic design.