Surrogate-Assisted Optimization and Multi-Criteria Decision-Making for the Design of a Bridge-Type Compliant Mechanism

Bridge-type compliant mechanisms (BCMs) are widely utilized in micro- and nano-positioning systems for motion amplification. Balancing performance across amplification capability, stiffness, stress, and structural compactness for a BCM is challenging because of the coupled and competing effects among these performance metrics. This paper presents an integrated framework that combines surrogate modeling, multi-objective optimization, and multi-criteria decision-making for the application-specific optimization of BCMs. Geometric parameterization of the BCM is established to enable fast, automated finite element analysis. Optimal Latin hypercube sampling (OLHS) is used to generate the BCM sample set, whose pairwise correlations are assessed through correlation analysis. Based on the sample responses, the surrogate model with the highest predictive accuracy is selected from RSM, RBF, SVR, and Kriging. Four multi-objective optimization algorithms, MOPSO, SPEA2, NSGA-II, and NSGA-III, are compared in terms of convergence, solution distribution, and coverage to select the algorithm for subsequent optimization. The resulting Pareto solutions are ranked using AHP–EWM combined with TOPSIS. The framework is applied to the series design of three BCMs intended to provide graded output stiffnesses. The optimized designs are verified through finite element analysis and experimental study. The results demonstrate that the framework balances competing performance requirements and facilitates application-oriented series design of BCMs.