Chatter detection in milling process based on time-frequency analysis

Meng Kun Liu, Quang M. Tran, Yi Wen Qui, Chun-Hui Chung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Chatter identification is necessary in order to achieve stable machining conditions. However, the linear approximation in regenerative chatter vibration is problematic because of the rich nonlinear characteristics in machining. In this study, a novel method to detect chatter is proposed. Firstly, measured cutting force signals are decomposed into a set of intrinsic mode functions by using ensemble empirical mode decomposition. Hilbert transform is following to extract the instantaneous frequency. Fast Fourier transform is also utilized for each intrinsic mode function to determine the intrinsic mode function that contains rich chatter. Finally, the standard deviation and energy ratio in frequency domain of intrinsic mode functions are found as simply dimensionless chatter indicators. The effectively proposed approach is validated by analyzing the machined surface topography and also compared to the stability lobe diagram.

Original languageEnglish
Title of host publicationProcesses
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791850725
DOIs
Publication statusPublished - 2017 Jan 1
EventASME 2017 12th International Manufacturing Science and Engineering Conference, MSEC 2017 collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing - Los Angeles, United States
Duration: 2017 Jun 42017 Jun 8

Publication series

NameASME 2017 12th International Manufacturing Science and Engineering Conference, MSEC 2017 collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing
Volume1

Other

OtherASME 2017 12th International Manufacturing Science and Engineering Conference, MSEC 2017 collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing
CountryUnited States
CityLos Angeles
Period17-06-0417-06-08

Fingerprint

Machining
Surface topography
Fast Fourier transforms
Decomposition

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

Cite this

Liu, M. K., Tran, Q. M., Qui, Y. W., & Chung, C-H. (2017). Chatter detection in milling process based on time-frequency analysis. In Processes (ASME 2017 12th International Manufacturing Science and Engineering Conference, MSEC 2017 collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing; Vol. 1). American Society of Mechanical Engineers. https://doi.org/10.1115/MSEC20172712
Liu, Meng Kun ; Tran, Quang M. ; Qui, Yi Wen ; Chung, Chun-Hui. / Chatter detection in milling process based on time-frequency analysis. Processes. American Society of Mechanical Engineers, 2017. (ASME 2017 12th International Manufacturing Science and Engineering Conference, MSEC 2017 collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing).
@inproceedings{695ccc7357e84350913a1e1901207a22,
title = "Chatter detection in milling process based on time-frequency analysis",
abstract = "Chatter identification is necessary in order to achieve stable machining conditions. However, the linear approximation in regenerative chatter vibration is problematic because of the rich nonlinear characteristics in machining. In this study, a novel method to detect chatter is proposed. Firstly, measured cutting force signals are decomposed into a set of intrinsic mode functions by using ensemble empirical mode decomposition. Hilbert transform is following to extract the instantaneous frequency. Fast Fourier transform is also utilized for each intrinsic mode function to determine the intrinsic mode function that contains rich chatter. Finally, the standard deviation and energy ratio in frequency domain of intrinsic mode functions are found as simply dimensionless chatter indicators. The effectively proposed approach is validated by analyzing the machined surface topography and also compared to the stability lobe diagram.",
author = "Liu, {Meng Kun} and Tran, {Quang M.} and Qui, {Yi Wen} and Chun-Hui Chung",
year = "2017",
month = "1",
day = "1",
doi = "10.1115/MSEC20172712",
language = "English",
series = "ASME 2017 12th International Manufacturing Science and Engineering Conference, MSEC 2017 collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing",
publisher = "American Society of Mechanical Engineers",
booktitle = "Processes",

}

Liu, MK, Tran, QM, Qui, YW & Chung, C-H 2017, Chatter detection in milling process based on time-frequency analysis. in Processes. ASME 2017 12th International Manufacturing Science and Engineering Conference, MSEC 2017 collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing, vol. 1, American Society of Mechanical Engineers, ASME 2017 12th International Manufacturing Science and Engineering Conference, MSEC 2017 collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing, Los Angeles, United States, 17-06-04. https://doi.org/10.1115/MSEC20172712

Chatter detection in milling process based on time-frequency analysis. / Liu, Meng Kun; Tran, Quang M.; Qui, Yi Wen; Chung, Chun-Hui.

Processes. American Society of Mechanical Engineers, 2017. (ASME 2017 12th International Manufacturing Science and Engineering Conference, MSEC 2017 collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing; Vol. 1).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Chatter detection in milling process based on time-frequency analysis

AU - Liu, Meng Kun

AU - Tran, Quang M.

AU - Qui, Yi Wen

AU - Chung, Chun-Hui

PY - 2017/1/1

Y1 - 2017/1/1

N2 - Chatter identification is necessary in order to achieve stable machining conditions. However, the linear approximation in regenerative chatter vibration is problematic because of the rich nonlinear characteristics in machining. In this study, a novel method to detect chatter is proposed. Firstly, measured cutting force signals are decomposed into a set of intrinsic mode functions by using ensemble empirical mode decomposition. Hilbert transform is following to extract the instantaneous frequency. Fast Fourier transform is also utilized for each intrinsic mode function to determine the intrinsic mode function that contains rich chatter. Finally, the standard deviation and energy ratio in frequency domain of intrinsic mode functions are found as simply dimensionless chatter indicators. The effectively proposed approach is validated by analyzing the machined surface topography and also compared to the stability lobe diagram.

AB - Chatter identification is necessary in order to achieve stable machining conditions. However, the linear approximation in regenerative chatter vibration is problematic because of the rich nonlinear characteristics in machining. In this study, a novel method to detect chatter is proposed. Firstly, measured cutting force signals are decomposed into a set of intrinsic mode functions by using ensemble empirical mode decomposition. Hilbert transform is following to extract the instantaneous frequency. Fast Fourier transform is also utilized for each intrinsic mode function to determine the intrinsic mode function that contains rich chatter. Finally, the standard deviation and energy ratio in frequency domain of intrinsic mode functions are found as simply dimensionless chatter indicators. The effectively proposed approach is validated by analyzing the machined surface topography and also compared to the stability lobe diagram.

UR - http://www.scopus.com/inward/record.url?scp=85027686780&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85027686780&partnerID=8YFLogxK

U2 - 10.1115/MSEC20172712

DO - 10.1115/MSEC20172712

M3 - Conference contribution

T3 - ASME 2017 12th International Manufacturing Science and Engineering Conference, MSEC 2017 collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing

BT - Processes

PB - American Society of Mechanical Engineers

ER -

Liu MK, Tran QM, Qui YW, Chung C-H. Chatter detection in milling process based on time-frequency analysis. In Processes. American Society of Mechanical Engineers. 2017. (ASME 2017 12th International Manufacturing Science and Engineering Conference, MSEC 2017 collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing). https://doi.org/10.1115/MSEC20172712