Effective multi-sensor data fusion for chatter detection in milling process

Minh-Quang Tran; Meng-Kun Liu; Mahmoud Elsisi

doi:10.1016/j.isatra.2021.07.005

Effective multi-sensor data fusion for chatter detection in milling process

ISA Trans. 2022 Jun:125:514-527. doi: 10.1016/j.isatra.2021.07.005. Epub 2021 Jul 5.

Authors

Minh-Quang Tran¹, Meng-Kun Liu², Mahmoud Elsisi³

Affiliations

¹ Industry 4.0 Implementation Center, Center of Cyber-physical System Innovation, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan; Department of Mechanical Engineering, Thai Nguyen University of Technology, Thai Nguyen, Viet Nam. Electronic address: minhquang.tran@mail.ntust.edu.tw.
² Industry 4.0 Implementation Center, Center of Cyber-physical System Innovation, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan; Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan.
³ Industry 4.0 Implementation Center, Center of Cyber-physical System Innovation, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan; Department of Electrical Engineering, Faculty of Engineering (Shoubra), Benha University, 108 Shoubra St., B. O. Box 11241, Cairo, Egypt.

PMID: 34253339
DOI: 10.1016/j.isatra.2021.07.005

Abstract

This paper introduces a newly developed multi-sensor data fusion for the milling chatter detection with a cheap and easy implementation compared with traditional chatter detection schemes. The proposed multi-sensor data fusion utilizes microphone and accelerometer sensors to measure the occurrence of chatter during the milling process. It has the advantageous over the dynamometer in terms of easy installation and low cost. In this paper, the wavelet packet decomposition is adopted to analyze both measured sound and vibration signals. However, the parameters of the wavelet packet decomposition require fine-tuning to provide good performance. Hence the result of the developed scheme has been improved by optimizing the selection of the wavelet packet decomposition parameters including the mother wavelet and the decomposition level based on the kurtosis and crest factors. Furthermore, the important chatter features are selected using the recursive feature elimination method, and its performance is compared with metaheuristic algorithms. Finally, several machine learning techniques have been adopted to classify the cutting stabilities based on the selected features. The results confirm that the proposed multi-sensor data fusion scheme can provide an effective chatter detection under industrial conditions, and it has higher accuracy than the traditional schemes.

Keywords: Chatter detection; Machine learning; Multi-sensor fusion; Time–frequency analysis; Wavelet packet decomposition.