Pengfei Zhang
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
Authors
Dong Gao
Dongbo Hong
Yong Lu
Qian Wu
Shusong Zan
Dr. ZHIRONG LIAO ZHIRONG.LIAO@NOTTINGHAM.AC.UK
Associate Professor
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. |
Files
Improving Generalisation And Accuracy
(12 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
You might also like
What can micromechanics tell us about the surface integrity of shot-peened materials?
(2023)
Journal Article
Downloadable Citations
About Repository@Nottingham
Administrator e-mail: discovery-access-systems@nottingham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search