Cutting parameters have a significant impact on the machining *** order to reduce the machining time and improve the machining quality,this paper proposes an optimization algorithm based on bp neural networkImproved M...
详细信息
Cutting parameters have a significant impact on the machining *** order to reduce the machining time and improve the machining quality,this paper proposes an optimization algorithm based on bp neural networkImproved Multi-Objective Particle Swarm(bp-dwmopso).Firstly,this paper analyzes the existing problems in the traditional multi-objective particle swarm ***,the bp neural network model and the dynamic weight multi-objective particle swarm algorithm model are ***,the bp-dwmopso algorithm is designed based on the established *** order to verify the effectiveness of the algorithm,this paper obtains the required data through equal probability orthogonal experiments on a typical Computer Numerical Control(CNC)turning machining case and uses the bp-dwmopso algorithm for *** experimental results show that the Cutting speed is 69.4 mm/min,the Feed speed is 0.05 mm/r,and the Depth of cut is 0.5 *** results show that the bp-dwmopso algorithm can find the cutting parameters with a higher material removal rate and lower spindle load while ensuring the machining *** method provides a new idea for the optimization of turning machining parameters.
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