Constrained multiobjective robust optimization of a bistable mechanism for inertial switch

This paper proposes an optimization method for finding the optimal design of a bistable mechanism with a desired performance that is robust to structural and material uncertainties. Using interval numbers to characterize the uncertainties in the structural parameters and materials, we present a nonprobabilistic multiobjective optimization model and transform it into a single objective optimization model using a penalty function. The sensitivity of the mechanical performance of bistable structures to uncertain parameters was analyzed, and the design parameters with notable effects on the bistable performance were identified as optimization variables. A neural network-based proxy model for the nonlinear characteristics of the bistable mechanism was established, and its accuracy was validated through finite element outcomes. Based on this model, a two-layer nested genetic algorithm was employed to solve the multiobjective robust optimization problem of the bistable structures with critical forces and a second stable position. The effectiveness of the optimization method was verified by comparing it with the finite element and experimental results. The proposed method was applied in the design of silicon-based inertial switches.