Detecting machine chatter using audio data and machine learning

被引：10

作者：

Kvinevskiy, Ilarion ^{[1
]}

Bedi, Sanjeev ^{[1
]}

Mann, Stephen ^{[2
]}

机构：

[1] Univ Waterloo, Dept Mech & Mech Engn, 200 Univ Ave W, Waterloo, ON N2L 3G1, Canada

[2] Univ Waterloo, Cheriton Sch Comp Sci, 200 Univ Ave W, Waterloo, ON N2L 3G1, Canada

来源：

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY | 2020年 / 108卷 / 11-12期

基金：

加拿大自然科学与工程研究理事会;

关键词：

CNC machining; Chatter; Machine learning;

D O I：

10.1007/s00170-020-05571-9

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We present a method for detecting chatter in CNC machining. Our method uses machining learning to train a classifier to determine the chatter threshold, and we use an autoencoder to reduce the dimensionality of the data. We test our method on machining audio data, and successfully detect chatter in the validation data. Our method is amenable to use on the shop floor, as a machinist using our method needs only to classify audio as chatter and non-chatter.

引用

页码：3707 / 3716

页数：10

共 50 条

[1] Detecting machine chatter using audio data and machine learning
Ilarion Kvinevskiy
Sanjeev Bedi
Stephen Mann
[J]. The International Journal of Advanced Manufacturing Technology, 2020, 108 : 3707 - 3716
[2] Chatter Classification in Turning using Machine Learning and Topological Data Analysis
Khasawneh, Firas A.
Munch, Elizabeth
Perea, Jose A.
[J]. IFAC PAPERSONLINE, 2018, 51 (14): : 195 - 200
[3] Detecting Mislabeled Data Using Supervised Machine Learning Techniques
Poel, Mannes
[J]. AUGMENTED COGNITION: NEUROCOGNITION AND MACHINE LEARNING, AC 2017, PT I, 2017, 10284 : 571 - 581
[4] Detecting Anomalies in Financial Data Using Machine Learning Algorithms
Bakumenko, Alexander
Elragal, Ahmed
[J]. SYSTEMS, 2022, 10 (05):
[5] Utilizing Machine Learning for Detecting Harmful Situations by Audio and Text
Allouch, Merav
Mansbach, Noa
Azaria, Amos
Azoulay, Rina
[J]. APPLIED SCIENCES-BASEL, 2023, 13 (06):
[6] Machine Learning for Detecting Anomalies in SAR Data
Haitman, Yuval
Berkovich, Itay
Havivi, Shiran
Maman, Shimrit
Blumberg, Dan G.
Rotman, Stanley R.
[J]. 2019 IEEE INTERNATIONAL CONFERENCE ON MICROWAVES, ANTENNAS, COMMUNICATIONS AND ELECTRONIC SYSTEMS (COMCAS), 2019,
[7] Machine Learning for Detecting Data Exfiltration: A Review
Sabir, Bushra
Ullah, Faheem
Babar, M. Ali
Gaire, Raj
[J]. ACM COMPUTING SURVEYS, 2022, 54 (03)
[8] Detecting Keratoconus From Corneal Imaging Data Using Machine Learning
Lavric, Alexandru
Popa, Valentin
Takahashi, Hidenori
Yousefi, Siamak
[J]. IEEE ACCESS, 2020, 8 : 149113 - 149121
[9] Detecting Data Injection Attacks in ROS Systems using Machine Learning
Antunes, Rodrigo Abrantes
Dalmazo, Bruno L.
Drews-Jr, Paulo L. J.
[J]. 2022 LATIN AMERICAN ROBOTICS SYMPOSIUM (LARS), 2022 BRAZILIAN SYMPOSIUM ON ROBOTICS (SBR), AND 2022 WORKSHOP ON ROBOTICS IN EDUCATION (WRE), 2022, : 223 - 228
[10] Chatter prediction in boring process using machine learning technique
[J]. Saravanamurugan, S. (s_saravana@cb.amrita.edu), 2017, Inderscience Publishers, 29, route de Pre-Bois, Case Postale 856, CH-1215 Geneva 15, CH-1215, Switzerland (12)

← 1 2 3 4 5 →