A Two-stage Optimization System for Cutting Parameters

  Based on the fine boring of the bearing holes of the auto bearing support, Taguchi experiments, grey relational analysis, the backpropagation neural network and particle swarm optimization are employed to optimize the cutting parameters. The twostage optimization system can rapidly obtain the best cutting parameter settings to improve the quality of components and the stability of processing. The result demonstrates the feasibility and effectiveness of the proposed approach. It provides a novel approach and pathway for mechanical processing enterprises to enhance their competitiveness.