A Data-Driven Approach to the Dimensional Synthesis of Planar Slider–Crank Function Generators

This study presents a data-driven, machine learning-based approach to the dimensional synthesis of planar four-link slider–crank function generators. The proposed methodology integrates kinematic analysis to generate physically feasible datasets that capture the relationship between linkage dimensions and the precision points of slider–crank linkages. To synthesize valid, defect-free linkages for an arbitrary number of user-defined precision points, a customized Long Short-Term Memory (LSTM)-based model is developed and trained on the generated dataset. A parameterization scheme for the linkage dimensions is introduced to ensure prediction-level validity, enabling stable convergence and physically realizable predictions. Numerical results demonstrate high accuracy and robustness under both absolute and relative precision-point specifications, despite the model being trained solely on absolute precision points without any initial configuration estimation. In addition to deriving feasible linkage dimensions, the proposed method offers a practical and scalable framework for engineering design applications.