Conceptual synthesis of mechanisms based on qualitative reasoning

This paper presents an innovative method for qualitative reasoning in the conceptual synthesis of mechanisms, leveraging AI-derived knowledge-based principles. The methodology allows for discretizing a mechanism's overall behavior without computational implementation. Relative motion between components, represented as qualitative states, is captured in a qualitative motion vector. These vectors form a general movement matrix that characterizes the mechanism's behavior, providing insights into component functions, movements, and transitions. By using ratchets as restriction functions, underlying behaviors are isolated from the matrix. These behaviors are used to generate conceptual designs for new mechanisms fulfilling specific kinematic functions. A case study is presented: synthesizing a mechanism that converts oscillatory rotation into unidirectional rotation using a differential gear train. The qualitative behavior of the resulting design is visualized in a vector diagram and compared with a CAD simulation. This method provides a knowledge base for training AI models in conceptual synthesis without needing specialized software. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1208Dimensions.Open Alex.