Wear prediction of micro-grinding tool based on GA-BP neural network

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 detection 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 evaluate 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, cutting 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 different 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 under different process parameters and grinding lengths. It has higher prediction accuracy during the self-learning process, with an average error of 5% .