木勺毛坯捆抓取柔性夹持器结构优化OA

To address the low efficiency of manual water immersion and the susceptibility to damage of wooden spoon blanks in the molding process,this paper presents the design of an adaptive flexible gripper for grasping wooden spoon blank bundles.An optimized experimental design was developed using finite element analysis software.A regression pre-diction model for the gripper's strain energy was constructed using a Random Forest(RF)method optimized by a Bayes-ian optimization(BO)algorithm,termed BO-RF.Furthermore,the explainable machine learning method,SHAP(SHapley Additive exPlanations),was employed to provide both global and local interpretability analysis of the model.Based on this predictive model,a genetic algorithm(GA)was utilized to perform an optimal design of the flexible grip-per,with the conflicting objectives of maximizing the top surface strain energy and minimizing the overall strain energy.The multi-objective Pareto front was computed using the BO-RF model.Simulation results validate the effectiveness and feasibility of the proposed modeling and optimization methodology.