CN 41-1243/TG ISSN 1006-852X
Volume 44 Issue 3
Jun.  2024
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Article Contents
TIAN Miao, YU Kangning, REN Yinghui, SHE Chengxi, YI Luan. Wear prediction of micro-grinding tool based on GA-BP neural network[J]. Diamond & Abrasives Engineering, 2024, 44(3): 363-373. doi: 10.13394/j.cnki.jgszz.2023.0074
Citation: TIAN Miao, YU Kangning, REN Yinghui, SHE Chengxi, YI Luan. Wear prediction of micro-grinding tool based on GA-BP neural network[J]. Diamond & Abrasives Engineering, 2024, 44(3): 363-373. doi: 10.13394/j.cnki.jgszz.2023.0074

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

doi: 10.13394/j.cnki.jgszz.2023.0074
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  • Received Date: 2023-03-25
  • Accepted Date: 2023-06-07
  • Rev Recd Date: 2023-06-07
  • Available Online: 2024-06-28
  • 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% .


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