Chatter identifi cation of thin-walled parts for intelligent manufacturing based on multi-signal processing

doi:10.1007/s40436-020-00299-x

Advances in Manufacturing ›› 2021, Vol. 9 ›› Issue (1): 22-33.doi: 10.1007/s40436-020-00299-x

• ARTICLES • 上一篇

Chatter identifi cation of thin-walled parts for intelligent manufacturing based on multi-signal processing

Dong-Dong Li¹, Wei-Min Zhang^1,2, Yuan-Shi Li³, Feng Xue¹, Jürgen Fleischer^1,4

1 School of Mechanical Engineering, Tongji University, Shanghai 201804, People's Republic of China;
2 Bosch Rexroth Endowed Chair for Automation & Electrification Solutions, Sino-German College for Postgraduate Study, Tongji University, Shanghai 201804, People's Republic of China;
3 China North Engine Research Institute, Tianjin 300400, People's Republic of China;
4 WBK Institute of Production Science, Karlsruhe Institute of Technology, Karlsruhe 76131, Germany

收稿日期:2019-06-14 修回日期:2019-12-12 发布日期:2021-02-27
通讯作者: Dong-Dong Li E-mail:1510289@tongji.edu.cn
基金资助:
Authors acknowledge the support from the National Key R&D Program of China (Grant No. 2017YFE0101400), and also appreciate reviewers for their critical comments.

Chatter identifi cation of thin-walled parts for intelligent manufacturing based on multi-signal processing

Dong-Dong Li¹, Wei-Min Zhang^1,2, Yuan-Shi Li³, Feng Xue¹, Jürgen Fleischer^1,4

1 School of Mechanical Engineering, Tongji University, Shanghai 201804, People's Republic of China;
2 Bosch Rexroth Endowed Chair for Automation & Electrification Solutions, Sino-German College for Postgraduate Study, Tongji University, Shanghai 201804, People's Republic of China;
3 China North Engine Research Institute, Tianjin 300400, People's Republic of China;
4 WBK Institute of Production Science, Karlsruhe Institute of Technology, Karlsruhe 76131, Germany

Received:2019-06-14 Revised:2019-12-12 Published:2021-02-27
Contact: Dong-Dong Li E-mail:1510289@tongji.edu.cn
Supported by:
Authors acknowledge the support from the National Key R&D Program of China (Grant No. 2017YFE0101400), and also appreciate reviewers for their critical comments.

摘要/Abstract

摘要： Machine chatter is still an unresolved and challenging issue in the milling process, and developing an online chatter identification and process monitoring system towards smart manufacturing is an urgent requirement. In this paper, two indicators of chatter detection are investigated. One is the real-time variance of milling force signals in the time domain, and the other one is the wavelet energy ratio of acceleration signals based on wavelet packet decomposition in the frequency domain. Then, a novel classification concept for vibration condition, called slight chatter, is proposed and integrated successfully into the designed multi-classification support vector machine (SVM) model. Finally, a mapping model between image and chatter indicators is established via a distance threshold on the image. The multi-SVM model is trained by the results of three signals as an input. Experiment data and detection accuracy of the SVM model are verified in actual machining. The identification accuracy of 96.66% has proved that the proposed solution is feasible and effective. The presented method can be used to select optimized milling parameters to improve machining process stability and strengthen manufacturing system monitoring.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-020-00299-x

关键词: Chatter, Milling force, Acceleration, Wavelet packet decomposition, Multi-sensor

Abstract: Machine chatter is still an unresolved and challenging issue in the milling process, and developing an online chatter identification and process monitoring system towards smart manufacturing is an urgent requirement. In this paper, two indicators of chatter detection are investigated. One is the real-time variance of milling force signals in the time domain, and the other one is the wavelet energy ratio of acceleration signals based on wavelet packet decomposition in the frequency domain. Then, a novel classification concept for vibration condition, called slight chatter, is proposed and integrated successfully into the designed multi-classification support vector machine (SVM) model. Finally, a mapping model between image and chatter indicators is established via a distance threshold on the image. The multi-SVM model is trained by the results of three signals as an input. Experiment data and detection accuracy of the SVM model are verified in actual machining. The identification accuracy of 96.66% has proved that the proposed solution is feasible and effective. The presented method can be used to select optimized milling parameters to improve machining process stability and strengthen manufacturing system monitoring.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-020-00299-x

Key words: Chatter, Milling force, Acceleration, Wavelet packet decomposition, Multi-sensor

Dong-Dong Li, Wei-Min Zhang, Yuan-Shi Li, Feng Xue, Jürgen Fleischer. Chatter identifi cation of thin-walled parts for intelligent manufacturing based on multi-signal processing[J]. Advances in Manufacturing, 2021, 9(1): 22-33.

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Chatter identifi cation of thin-walled parts for intelligent manufacturing based on multi-signal processing

Chatter identifi cation of thin-walled parts for intelligent manufacturing based on multi-signal processing

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