用数据驱动方法直接生成平面四杆机构，无需复杂计算。
Data-Driven Dimensional Synthesis of Diverse Planar Four-bar Function Generation Mechanisms via Direct Parameterization
- 通过神经网络直接预测机构尺寸，跳过传统求解过程
- 在多种构型下生成精度高且无缺陷的机构链接
- 适合非专业人士快速设计，适用于大规模灵活合成
平面四杆机构的尺寸综合是运动学中的一个挑战性逆问题，需从期望运动特性中确定机构尺寸。本文提出一种数据驱动框架，摒弃传统方程求解与优化，转而采用监督学习。方法结合合成数据集、基于LSTM的序列精度点处理网络，以及针对不同连杆类型定制的专家混合（MoE）架构。每个专家模型在特定类型数据上训练，并由类型指定层引导，实现单类型与多类型综合。提出一种新型仿真评估指标，通过比较期望运动与生成运动来衡量预测质量。实验表明，该方法可在多种配置下生成准确且无缺陷的连杆结构，使机构设计更直观高效，即使对非专业用户也适用，为运动学设计中的可扩展、灵活综合开辟新路径。
原文摘要 · Abstract (English)
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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