Modeling and Optimization of Drilling Process Parameters of Fluorophlogopite Ceramics Based on Improved BP Neural Network with WOA Algorithm
The material removal and tool wear under different processing parameters were measured by drilling experiments of fluorophlogopite ceramics The WOA algorithm is used to optimize the BP neural network.Based on the single factor experimental value and the prediction value of the WOA-BP network,a unitary model of the material removal rate and tool wear rate with respect to the process parameters is es-tablished by using the least square fitting method,and the accuracy of the model is checked by the correla-tion coefficient a multivariate model is proposed on the basis of the unitary model.The multivariate model is solved based on orthogonal experimental values and WOA algorithm.The model error is within a reason-able range taking the maximum material removal rate and the minimum tool wear rate as the optimization objectives,the WOA algorithm was used to optimize the process parameters and a set of optimal parameters were obtained Based on the optimal process parameters,the validation experiment results show that the opti-mal parameters are reasonable.
NETL
NSTL
万方数据
维普
