面向残余应力控制的高温合金铣削刀具几何参数优化方法

Optimization of High-Temperature Alloy Milling Tool Geometry for Residual Stress Control

针对GH4169铣削过程,采用正交试验获得了不同刀具结构参数下的表面残余应力。利用遗传算法(GA)优化了BP神经网络的初始权值和阈值,提高了模型的收敛速度和预测精度,并提出应用GA-BP模型预测铣削残余应力的方法。研究了基于萤火虫算法(FA)进行工艺参数优化的方法,结合GA-BP预测模型,建立了铣削残余应力的GA-BP-FA参数优化模型,并以同时获得最小残余拉应力/最大残余压应力为目标,进行刀具几何参数的多目标优化。结果表明,采用优化后的刀具几何参数,可以获得X方向的最小残余拉应力、Y方向的最大残余压应力。

With regard to the milling process of GH4169,the surface residual stresses under different tool structural parameters were obtained based on orthogonal experimental method.The initial weights and thresholds of the BP neural network were optimized using a genetic algorithm(GA)to improve the convergence speed and prediction accuracy of the model,and a method for applying the GA–BP model to predict the milling residual stress was proposed.The firefly algorithm(FA)–based method for process parameter optimization was investigated,and the GA–BP–FA parameter optimization model for milling residual stresses was established in combination with the GA–BP prediction model for multi-objective optimization of tool geometry parameters with the goal of simultaneously obtaining the minimum residual tensile stress/maximum residual compressive stress.The results show that the minimum residual tensile stress in the X–direction and the maximum residual compressive stress in the Y–direction can be obtained using the optimized tool geometry parameters.