Abstract
Extracting discriminative tool wear features is of great importance for tool wear monitoring in micro-milling. However, due to the dependency on tool runout and cutting parameters, the traditional tool wear features are incompetent to monitor the tool wear condition in micro-milling with significant tool runout and varied cutting parameter interactions. In this study, micro-milling cutting force is represented by a parametric model including variable cutting parameters, tool runout, and tool wear. The cutting force coefficient in the model, which is not only discriminative to the tool wear condition but also independent to the tool runout and cutting parameters, is extracted as the micro-milling tool wear feature. To reduce the computation cost, a fast neural network–based method is proposed to identify the tool runout and the cutting force coefficient from the cutting force signal. Experimental results show that the proposed cutting force coefficient–based approach is efficient to monitor the micro-milling tool wear under varied cutting parameters and tool runout.
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Acknowledgments
The first author would like to thank the Lab of Precision Manufacturing, Institute of Advanced Manufacturing Technology, Chinese Academy of Sciences, for providing the experimental data.
Funding
This project is supported by The Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No.19KJB460007), National Natural Science Foundation of China (Grant No. 51805341), and Natural Science Foundation of Jiangsu Province (Grant No. BK20180843).
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Liu, T., Zhu, K. & Wang, G. Micro-milling tool wear monitoring under variable cutting parameters and runout using fast cutting force coefficient identification method. Int J Adv Manuf Technol 111, 3175–3188 (2020). https://doi.org/10.1007/s00170-020-06272-z
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DOI: https://doi.org/10.1007/s00170-020-06272-z