Research into Tool Wear Monitoring Using Multi-Signal Fusion Based on an Integrated Machine Learning Model

Accurate tool wear monitoring can effectively improve machining quality and reduce tool costs. In this paper, tool wear monitoring was studied using multi-signal fusion based on an integrated machine learning model. Firstly, tool holder strain, acceleration, and AE signals are selected as tool wear monitoring signals based on different types of physical quantities and acceptable installation convenience. Tool wear experiments are conducted to synchronously acquire these signals. After the signal denoising process, 102 features from these signals are extracted, which include time domain, frequency domain, and wavelet packet time-frequency domain features. Then, 15 key features are selected using the minimum redundancy maximum relevance (mRMR) method to realize multi-signal fusion at the feature level. Subsequently, an integrated machine learning model is proposed for tool wear monitoring. Three complementary models, extra trees, random forest, and ridge regression, are selected to construct the integrated model. The results indicate that this strategy achieves a tool wear state classification accuracy of 96.77%, exhibiting higher accuracy than single models.