research-article

ACWGAN-GP for milling tool breakage monitoring with imbalanced data

Authors:

Lihui WangAuthors Info & Claims

Volume 85, Issue C

https://doi.org/10.1016/j.rcim.2023.102624

Published: 01 February 2024 Publication History

Highlights

•

A novel tool breakage monitoring method based on ACWGAN-GP was proposed.

•

A sample filter was established to ensure the quality of the generated samples.

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Artificially controlled tool breakage monitoring experiments were carried out.

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The data imbalance problem of tool breakage monitoring was effectively addressed.

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The accuracy and real-time performance of the proposed method for tool breakage monitoring were verified.

Abstract

Tool breakage monitoring (TBM) during milling operations is crucial for ensuring workpiece quality and minimizing economic losses. Under the premise of sufficient training data with a balanced distribution, TBM methods based on statistical analysis and artificial intelligence enable accurate recognition of tool breakage conditions. However, considering the actual manufacturing safety, cutting tools usually work in normal wear conditions, and acquiring tool breakage signals is extremely difficult. The data imbalance problem seriously affects the recognition accuracy and robustness of the TBM model. This paper proposes a TBM method based on the auxiliary classier Wasserstein generative adversarial network with gradient penalty (ACWGAN-GP) from the perspective of data generation. By introducing Wasserstein distance and gradient penalty terms into the loss function of ACGAN, ACWGAN-GP can generate multi-class fault samples while improving the network's stability during adversarial training. A sample filter based on multiple statistical indicators is designed to ensure the quality and diversity of the generated data. Qualified samples after quality assessment are added to the original imbalanced dataset to improve the tool breakage classifier's performance. Artificially controlled face milling experiments for TBM are carried out on a five-axis CNC machine to verify the effectiveness of the proposed method. Experimental results reveal that the proposed method outperforms other popular imbalance fault diagnosis methods in terms of data generation quality and TBM accuracy, and can meet the real-time requirements of TBMs.

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Cited By

Wang RSong QPeng YQin JLiu ZLiu Z(2024)Toward digital twins for high-performance manufacturingRobotics and Computer-Integrated Manufacturing10.1016/j.rcim.2024.10272388:COnline publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1016/j.rcim.2024.102723
Wang ZZhang SZhang HZhang YLiang JHuang RHuang B(2024)Machining feature process route planning based on a graph convolutional neural networkAdvanced Engineering Informatics10.1016/j.aei.2023.10224959:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.aei.2023.102249

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Published In

cover image Robotics and Computer-Integrated Manufacturing

Robotics and Computer-Integrated Manufacturing Volume 85, Issue C

Feb 2024

399 pages

ISSN:0736-5845

Issue’s Table of Contents

Copyright © 2023.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 February 2024

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Cited By

Wang RSong QPeng YQin JLiu ZLiu Z(2024)Toward digital twins for high-performance manufacturingRobotics and Computer-Integrated Manufacturing10.1016/j.rcim.2024.10272388:COnline publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1016/j.rcim.2024.102723
Wang ZZhang SZhang HZhang YLiang JHuang RHuang B(2024)Machining feature process route planning based on a graph convolutional neural networkAdvanced Engineering Informatics10.1016/j.aei.2023.10224959:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.aei.2023.102249

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