Wear monitoring of micro-milling tools based on Improved Siamese Neural Network

Abstract

Micro-milling is employed for the precise machining of small components with intricate characteristics, and the quality of micro-milling is significantly influenced by the uncertainty surrounding tool wear. Monitoring the wear status of micro-milling tools holds great significance in enhancing machining efficiency and sustainability. In this work, wear monitoring of micro-milling tools method was proposed based on Improved Siamese Neural Network. The Improved Siamese Neural Network used two different global feature extraction networks. The features were input into the t improved Siamese neural network model for similarity score calculation and anomaly detection, which improves the detection accuracy of the abnormal state of micro-milling tool wear. In addition, Generative Adversarial Network (GAN) data augmentation is used to address the issue of abnormal data imbalance during the data preparation stage. The experiments show that the classification accuracy and the precision of the model on the micro-milling tool wear dataset is 92.15% and 93.32% respectively, indicating that the model has good stability and effectiveness.