金刚石与磨料磨具工程2024，Vol.44Issue(3)：363-373,11.DOI:10.13394/j.cnki.jgszz.2023.0074
基于GA-BP神经网络的微磨具磨损预测研究
Wear prediction of micro-grinding tool based on GA-BP neural network
摘要
Abstract
An intelligent tool wear prediction model has been proposed for the micro-grinding tool,optimized using a genetic algorithm(GA)based BP neural network.The GA-BP prediction model is applied with in-situ tool wear detec-tion to obtain training set data and combines cluster analysis to divide the tool wear stages.To represent the uncertainty in wear characteristics,the loss of cross-sectional area of the micro-grinding tool has been selected as an index to evalu-ate tool wear loss.The K-means clustering algorithm is used to cluster and analyze the tool wear stages under different process parameters.The GA-BP neural network includes five neurons in the input layer:rotating speed,feed rate,cut-ting depth,grinding length,and the initial cross-sectional area of the tool.The output layer neuron predicts the loss of the tool's cross-sectional area.To validate the method,a series of micro-grinding experiments were performed under dif-ferent parameters for the micro-groove array of monocrystalline silicon.The loss of the tool's cross-sectional area was measured by a self-made visual inspection system,providing learning samples for the prediction model.The predicted results of the GA-BP neural network model were compared with the traditional Gaussian process regression method.The results show that the GA-BP neural network model can correctly predict tool wear loss and identify wear stages un-der different process parameters and grinding lengths.It has higher prediction accuracy during the self-learning process,with an average error of 5%.关键词
微磨具/磨损预测/GA-BP神经网络/聚类分析Key words
micro-grinding tools/wear prediction/GA-BP neural network/cluster analysis分类
机械制造引用本文复制引用
田苗,于康宁,任莹晖,佘程熙,易峦..基于GA-BP神经网络的微磨具磨损预测研究[J].金刚石与磨料磨具工程,2024,44(3):363-373,11.基金项目
国家自然科学基金(52075161) （52075161）
