Transfer Learning‐Based Stiffness Modelling of Spherical Scissor Linkage Remote‐Centre‐of‐Motion Mechanism for Structural Optimisation

BACKGROUND: The spherical scissor linkage remote-centre-of-motion (SSL-RCM) mechanism has multiple joints and a low stiffness, severely affecting its load-bearing capacity and motion accuracy. Accurate stiffness modelling of the SSL-RCM mechanism is key to guide its design. METHODS: This paper uses the virtual joint method to establish a stiffness model considering passive joints. Applying transfer learning, small sample finite element data is extended by fitting a large dataset of simulation models, yielding a high-accuracy stiffness model for the SSL-RCM mechanism. A multi-objective genetic algorithm considering stiffness factors is used for structural optimisation of the SSL-RCM mechanism. RESULTS: Comparative results show that the stiffness model achieves a 12.2% higher fitting accuracy than the baseline model, and an SSL-RCM mechanism with improved overall performance is obtained. CONCLUSIONS: The stiffness model establishes an analytical relationship between the geometric dimensions and corrected stiffness of the SSL-RCM, which can be used for structural optimisation and design guidance.