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

• URL: http://arxiv.org/abs/2507.08269v1
• Date: Fri, 11 Jul 2025 02:32:29 GMT
• Title: Data-Driven Dimensional Synthesis of Diverse Planar Four-bar Function Generation Mechanisms via Direct Parameterization
• Authors: Woon Ryong Kim, Jaeheun Jung, Jeong Un Ha, Donghun Lee, Jae Kyung Shim,
• Abstract summary: We propose a data-driven framework that bypasses traditional equation-solving and optimization by leveraging supervised learning.<n>Our method combines a synthetic dataset, an LSTM-based neural network for handling sequential precision points, and a Mixture of Experts architecture tailored to different linkage types.<n> Experiments show our approach produces accurate, defect-free linkages across various configurations.
• Score: 2.499517394718329
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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