Multi-objective Optimization Design of Opening Position for 3D Turning Force Sensor Based on RSM and GWO-BP Hybrid Agent Model
The development of an intelligent CNC lathe requires the integration of an intelligent cutting force sensor to effec-tively monitor the real-time changes in cutting force during the cutting process and promptly assess the cutting status of the work-piece and tool.In this paper,aiming at the shortcomings of the sensitivity of the ten-angle ring cutting force sensor(bridge arm stress is too small),an approach involving opening a hole in the ring arm was employed to enhance the local stress within the structure.To determine the optimal position for the opening,a high-dimensional parameter space was sampled using the optimal space filling method,with a small deviation of the center CL2 value(CL2 =0.028).Compare the advantages of each of the two models,a hybrid agent model,combining RSM and GWO-BP,was constructed.The deformation,intrinsic frequency,and path stress of the sensor were considered as optimization objectives.To select the most suitable algorithm for multi-objective optimiza-tion of the hybrid agent model,the Pareto front,IGD,and HV of different algorithms were compared.SparseEA was chosen as the preferred algorithm for the multi-objective optimization.The optimized sensor exhibits a 14.7%increase in deformation and a sig-nificant 155%increase in local stress,about six times greater sensitivity in all three directions.
neural networkPareto frontierturning force sensormuti-objective optimizationcenter CL2 deviation
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