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Journal of Intelligent Manufacturing
Erschienen in: Journal of Intelligent Manufacturing 1/2022

22.09.2020

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

verfasst von: Yuqing Zhou, Bintao Sun, Weifang Sun, Zhi Lei

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 1/2022

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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.
Vorheriger Artikel Hierarchical multistrategy genetic algorithm for integrated process planning and scheduling
Nächster Artikel Smart sheet metal forming: importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking

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Metadaten
Titel
Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process
verfasst von
Yuqing Zhou
Bintao Sun
Weifang Sun
Zhi Lei
Publikationsdatum
22.09.2020
Verlag
Springer US
Erschienen in
Journal of Intelligent Manufacturing / Ausgabe 1/2022
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI
https://doi.org/10.1007/s10845-020-01663-1

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