{"ID":2895545,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.08269","arxiv_id":"2507.08269","title":"Data-Driven Dimensional Synthesis of Diverse Planar Four-bar Function Generation Mechanisms via Direct Parameterization","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.","short_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. Ou...","url_abs":"https://arxiv.org/abs/2507.08269","url_pdf":"https://arxiv.org/pdf/2507.08269v1","authors":"[\"Woon Ryong Kim\",\"Jaeheun Jung\",\"Jeong Un Ha\",\"Donghun Lee\",\"Jae Kyung Shim\"]","published":"2025-07-11T02:32:29Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"Mixture of Experts\"]","has_code":false}