A hybrid CNN-BiLSTM approach-based variational mode decomposition for tool wear monitoring

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-021-08448-7 ◽

2022 ◽

Author(s):

Rabah Bazi ◽

Tarak Benkedjouh ◽

Houssem Habbouche ◽

Said Rechak ◽

Noureddine Zerhouni

Keyword(s):

Tool Wear ◽

Variational Mode Decomposition ◽

Tool Wear Monitoring ◽

Mode Decomposition ◽

Wear Monitoring

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Cutting tool wear monitoring by Empirical Mode Decomposition method based on wavelet packet analysis

Advances in Engineering Materials and Applied Mechanics ◽

10.1201/b19268-65 ◽

2015 ◽

pp. 361-366

Keyword(s):

Tool Wear ◽

Empirical Mode Decomposition ◽

Decomposition Method ◽

Cutting Tool ◽

Wavelet Packet ◽

Tool Wear Monitoring ◽

Wavelet Packet Analysis ◽

Mode Decomposition ◽

Empirical Mode Decomposition Method ◽

Wear Monitoring

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An analysis of feature identification for tool wear monitoring by using acoustic emission

Traitement du signal ◽

10.3166/ts.34.117-135 ◽

2017 ◽

Vol 34 (3-4) ◽

pp. 117-135 ◽

Author(s):

D KONDALA RAO ◽

Kolla SRINIVAS

Keyword(s):

Acoustic Emission ◽

Tool Wear ◽

Tool Wear Monitoring ◽

Feature Identification ◽

Wear Monitoring

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Acoustic Emission Sensing for Tool Wear Monitoring and Process Control in Metal Cutting

Handbook of Design, Manufacturing and Automation ◽

10.1002/9780470172452.ch33 ◽

2007 ◽

pp. 695-707 ◽

Author(s):

Karthikeyan Chittayil ◽

Soundar R. T. Kumara ◽

Paul H. Cohen

Keyword(s):

Acoustic Emission ◽

Process Control ◽

Tool Wear ◽

Metal Cutting ◽

Tool Wear Monitoring ◽

Acoustic Emission Sensing ◽

Wear Monitoring

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Vibration-based tool wear monitoring using artificial neural networks fed by spectral centroid indicator and RMS of CEEMDAN modes

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-021-07376-w ◽

2021 ◽

Author(s):

Mourad Nouioua ◽

Mohamed Lamine Bouhalais

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Tool Wear ◽

Tool Wear Monitoring ◽

Spectral Centroid ◽

Wear Monitoring ◽

Artificial Neural

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Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network

Journal of Manufacturing Systems ◽

10.1016/j.jmsy.2021.06.006 ◽

2021 ◽

Vol 60 ◽

pp. 608-619

Author(s):

Xianli Liu ◽

Shaoyang Liu ◽

Xuebing Li ◽

Bowen Zhang ◽

Caixu Yue ◽

...

Keyword(s):

Tool Wear ◽

Short Term Memory ◽

Tool Wear Monitoring ◽

Term Memory ◽

Wear Monitoring ◽

Memory Network ◽

Long Short Term Memory

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Tool wear monitoring using acoustic emission in the existence of chatter

International Journal of Production Research ◽

10.1080/00207549008942839 ◽

1990 ◽

Vol 28 (10) ◽

pp. 1861-1869 ◽

Author(s):

YOICHI MATSUMOTO ◽

NGUN TJIANG ◽

BOBBIE FOOTE ◽

YNGVE NAERHEIMH

Keyword(s):

Acoustic Emission ◽

Tool Wear ◽

Tool Wear Monitoring ◽

Wear Monitoring

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Tool wear monitoring by using the imaginary part of the transfer function of the cutting dynamics

International Journal of Machine Tools and Manufacture ◽

10.1016/0890-6955(94)90008-6 ◽

1994 ◽

Vol 34 (3) ◽

pp. 393-406 ◽

Author(s):

Won-Tae Kwon ◽

Kornel F. Ehmann

Keyword(s):

Transfer Function ◽

Tool Wear ◽

Imaginary Part ◽

Tool Wear Monitoring ◽

Wear Monitoring

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An in-depth study of tool wear monitoring technique based on image segmentation and texture analysis

Measurement ◽

10.1016/j.measurement.2015.10.029 ◽

2016 ◽

Vol 79 ◽

pp. 44-52 ◽

Author(s):

Lihong Li ◽

Qingbin An

Keyword(s):

Image Segmentation ◽

Tool Wear ◽

Texture Analysis ◽

Tool Wear Monitoring ◽

Monitoring Technique ◽

Depth Study ◽

Wear Monitoring

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A back-propagation algorithm applied to tool wear monitoring

International Journal of Machine Tools and Manufacture ◽

10.1016/0890-6955(94)90047-7 ◽

1994 ◽

Vol 34 (5) ◽

pp. 625-631 ◽

Author(s):

S. Purushothaman ◽

Y.G. Srinivasa

Keyword(s):

Tool Wear ◽

Back Propagation ◽

Back Propagation Algorithm ◽

Tool Wear Monitoring ◽

Propagation Algorithm ◽

Wear Monitoring

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Tool wear monitoring of TC4 titanium alloy milling process based on multi-channel signal and time-dependent properties by using deep learning

Journal of Manufacturing Systems ◽

10.1016/j.jmsy.2021.09.017 ◽

2021 ◽

Vol 61 ◽

pp. 495-508

Author(s):

Boling Yan ◽

Lida Zhu ◽

Yichao Dun

Keyword(s):

Titanium Alloy ◽

Deep Learning ◽

Tool Wear ◽

Milling Process ◽

Time Dependent ◽

Tool Wear Monitoring ◽

Tc4 Titanium Alloy ◽

Wear Monitoring

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