Tool condition monitoring method by anomaly segmentation of time-frequency images using acoustic emission in small hole drilling
Author:
Affiliation:
1. Industrial Technology Center of SAGA
2. Kyushu Institute of Technology
Publisher
Japan Society of Mechanical Engineers
Subject
Industrial and Manufacturing Engineering,Mechanical Engineering
Link
https://www.jstage.jst.go.jp/article/jamdsm/17/3/17_2023jamdsm0034/_pdf
Reference28 articles.
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2. Beruvides, G., et al., Sensoring systems and signal analysis to monitor tool wear in microdrilling operations on a sintered tungsten–copper composite material, Sensors and Actuators A: Physical, Vol.199 (2013), pp.165-175.
3. Bradley, A. P., The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern recognition, Vol.30, No.7 (1997), pp.1145-1159.
4. Chen, X, and Beizhi Li., Acoustic emission method for tool condition monitoring based on wavelet analysis, The International Journal of Advanced Manufacturing Technology, Vol.33 (2007), pp.968-976.
5. Cohen, N., and Hoshen, Y., Sub-image anomaly detection with deep pyramid correspondences, arXiv preprint (2020), DOI:10.48550/arXiv.2005.02357.
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1. In Situ Measurement of the Machining State in Small-Diameter Drilling by Acoustic Emission Sensing;Coatings;2024-02-01
2. Tool state prediction model of Tent-ASO-BP neural network based on multi-feature fusion;Journal of Advanced Mechanical Design, Systems, and Manufacturing;2023
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