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Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process

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Abstract

Tool condition monitoring (TCM) in numerical control machines plays an essential role in ensuring high manufacturing quality. The TCM process is conducted according to the data obtained from one or more of a variety of sensors, among which acoustic sensors offer numerous practical advantages. However, acoustic sensor data suffer from strong noise, which can severely limit the accuracy of predictions regarding tool condition. The present work addresses this issue by proposing a novel TCM method that employs only a few appropriate feature parameters of acoustic sensor signals in conjunction with a two-layer angle kernel extreme learning machine. The two-layer network structure is applied to enhance the learning of features associated with complex nonlinear data, and two angle kernel functions without hyperparameters are employed to avoid the complications associated with the use of preset hyperparameters in conventional kernel functions. The proposed TCM method is experimentally demonstrated to achieve superior TCM performance relative to other state-of-the-art methods based on sound sensor data.

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Acknowledgements

The authors are grateful for support from the National Natural Science Foundation of China (Grant Nos. 51405346 and 71471139), the Zhejiang Provincial Natural Science Foundation of China (Grant No. LY17E050005), and the Wenzhou City Public Industrial Science and Technology Project of China (Grant Nos. 2018G0116). We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

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  1. College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, China

    Yuqing Zhou, Bintao Sun, Weifang Sun & Zhi Lei

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  1. Yuqing Zhou
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Correspondence to Weifang Sun.

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Zhou, Y., Sun, B., Sun, W. et al. Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process. J Intell Manuf 33, 247–258 (2022). https://doi.org/10.1007/s10845-020-01663-1

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