Abstract
In order to accurately monitor the tool wear degree in milling process, the tool wear state is monitored based on feature processing. Firstly, the force, vibration and acoustic emission signals were collected in the processing process, and the multi-dimensional information of the serial signals was obtained by analyzing the time domain, frequency domain and wavelet packet. Then, the Spearman coefficient was used to obtain the feature weight coefficient and extract the features strongly correlated with the average rear tool surface wear to carry out feature dimension reduction. Finally, KNN and ANN are respectively used to identify the tool wear state. By comparing the loss function, accuracy and confusion matrix, it is found that ANN model can accurately identify the tool wear state, and the recognition accuracy and generalization degree have been improved to some extent.
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Chao, L., Chen, W., Xiufeng, Z., Xuxiang, L., Yu, T. (2022). Research on Tool Wear State Monitoring Method Based on Feature Processing. In: Wang, Y., Martinsen, K., Yu, T., Wang, K. (eds) Advanced Manufacturing and Automation XI. IWAMA 2021. Lecture Notes in Electrical Engineering, vol 880. Springer, Singapore. https://doi.org/10.1007/978-981-19-0572-8_90
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DOI: https://doi.org/10.1007/978-981-19-0572-8_90
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