Data-Driven Dimensional Synthesis of Diverse Planar Four-bar Function Generation Mechanisms via Direct Parameterization
Journal:
arXiv
Published Date:
Jul 11, 2025
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.
