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Improving generalisation and accuracy of on-line milling chatter detection via a novel hybrid deep convolutional neural network

Zhang, Pengfei; Gao, Dong; Hong, Dongbo; Lu, Yong; Wu, Qian; Zan, Shusong; Liao, Zhirong

Improving generalisation and accuracy of on-line milling chatter detection via a novel hybrid deep convolutional neural network Thumbnail


Authors

Pengfei Zhang

Dong Gao

Dongbo Hong

Yong Lu

Qian Wu

Shusong Zan



Abstract

Unstable chatter seriously reduces the quality of machined workpiece and machining efficiency. In order to improve productivity, on-line chatter detection has attracted much interest in the past decades. Nevertheless, traditional methods are inevitably flawed due to the manually extracted features. Deep learning methods possess outstanding feature learning and classification capabilities, but the generalisation and accuracy are severely affected by the labelling and training of data. To address this, this paper proposed a novel hybrid deep convolutional neural network method combining an Inception module and a Squeeze-and-Excitation ResNet block (SR-block), namely ISR-CNN. The Inception module can automatically extract multi-scale features of cutting force signal to enrich the feature map. The SR-block can assign weights to different feature channels, thus suppressing useless feature maps and improving the model accuracy. Meanwhile, the introduction of SR-block also reduces the risk of gradient disappearance and speeds up the training of network. The generalisation and accuracy of the model is guaranteed by combining the two modules without training with transition state data. Milling tests were carried out on a wedge-shaped workpiece using different cutting parameters and tool overhang lengths to verify the accuracy and generalisability of the proposed method. The results showed that the proposed method outperforms other methods by achieving classification accuracy of on the validation and test sets 100% and 97.8%, respectively. In comparison to existing methods, the proposed method can correctly identify each machining state, including the transition states. Furthermore, the proposed method identifies the onset of chatter earlier than other methods, which is beneficial for chatter suppression.

Citation

Zhang, P., Gao, D., Hong, D., Lu, Y., Wu, Q., Zan, S., & Liao, Z. (2023). Improving generalisation and accuracy of on-line milling chatter detection via a novel hybrid deep convolutional neural network. Mechanical Systems and Signal Processing, 193, 110241. https://doi.org/10.1016/j.ymssp.2023.110241

Journal Article Type Article
Acceptance Date Feb 23, 2023
Online Publication Date May 4, 2023
Publication Date 2023-06
Deposit Date May 9, 2023
Publicly Available Date May 10, 2023
Journal Mechanical Systems and Signal Processing
Print ISSN 0888-3270
Electronic ISSN 1096-1216
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 193
Pages 110241
DOI https://doi.org/10.1016/j.ymssp.2023.110241
Keywords Chatter detection; Deep learning; Inception network; ResNet; Squeeze-and-excitation network
Public URL https://nottingham-repository.worktribe.com/output/19009529
Publisher URL https://www.sciencedirect.com/science/article/pii/S0888327023001486
Additional Information This article is maintained by: Elsevier; Article Title: Improving generalisation and accuracy of on-line milling chatter detection via a novel hybrid deep convolutional neural network; Journal Title: Mechanical Systems and Signal Processing; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.ymssp.2023.110241; Content Type: article; Copyright: © 2023 The Author(s). Published by Elsevier Ltd.

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