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The International Journal of Advanced Manufacturing Technology

Erschienen in:

04.06.2020 | ORIGINAL ARTICLE

Research on tool wear monitoring in drilling process based on APSO-LS-SVM approach

verfasst von: Ni Chen, Bijun Hao, Yuelong Guo, Liang Li, M. Aqib Khan, Ning He

Erschienen in: The International Journal of Advanced Manufacturing Technology | Ausgabe 7-8/2020

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Abstract

Tool wear monitoring is deemed as an essential technology of the intelligent manufacturing to guarantee the processing quality and improve the machining efficiency. In this paper, a prediction model based on adaptive particle swarm optimization (APSO) algorithm and least squares support vector machine (LS-SVM) algorithm is proposed for the recognition of drill wear. Cutting force signal and vibration signal are used for tool wear monitoring. And these signals are preprocessed through wavelet threshold de-noising algorithm. Multiple signal feature extraction methods are carried out to process the sample data related to drill wear status. The mean absolute error of the tool wear recognition model is 0.91%, better than the standard LS-SVM algorithm under the same condition.

Vorheriger Artikel Study on chip morphology and surface roughness in ultrasonically assisted drilling of 304 stainless steel

Nächster Artikel Analysis and design optimization of double-sided deep cold rolling process of a Ti-6Al-4V blade

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Titel: Research on tool wear monitoring in drilling process based on APSO-LS-SVM approach
verfasst von: Ni Chen
Bijun Hao
Yuelong Guo
Liang Li
M. Aqib Khan
Ning He
Publikationsdatum: 04.06.2020
Verlag: Springer London
Erschienen in: The International Journal of Advanced Manufacturing Technology / Ausgabe 7-8/2020
Print ISSN: 0268-3768
Elektronische ISSN: 1433-3015
DOI: https://doi.org/10.1007/s00170-020-05549-7

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