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Overview of titanium alloy cutting based on machine learning

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Abstract

Titanium alloy is an indispensable material in many industrial fields owing to its excellent strength, corrosion resistance, and heat resistance. Cutting is an important process in the manufacturing of titanium alloy; thus, it is necessary to monitor titanium alloy cutting, especially the monitoring of cutting tools. Since traditional monitoring is realized through operators, it is affected by the operator’s technology and cannot keep up with the modern-day precision manufacturing of titanium alloy. With recent technological developments, more and more attention is being paid to online monitoring, particularly by utilizing the field of machine learning to predict tool life. Accordingly, this paper first describes the challenges encountered in titanium alloy cutting. Then, it introduces the characteristics of several machine learning models and their application in monitoring titanium alloy cutting, along with a discussion on the advantages and disadvantages of such algorithms. Finally, an outlook on the future prospects of machine-learning-enabled monitoring of titanium alloy cutting is provided.

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The data used to support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

The authors would like to appreciate the National Natural Science Foundation of China (52105178, 12162008) and the Natural Science Foundation of Hunan Province (2022JJ40056).

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  1. College of Mechanical Engineering, Guizhou University, Guiyang, 550025, China

    YongLong Chen, Weilong Wu & Houfu Dai

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  1. YongLong Chen
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Chen, Y., Wu, W. & Dai, H. Overview of titanium alloy cutting based on machine learning. Int J Adv Manuf Technol 126, 4749–4762 (2023). https://doi.org/10.1007/s00170-023-11475-1

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