research-article

Monitoring of a machining process using kernel principal component analysis and kernel density estimation

Authors:

Gamini P. Mendis,

Matthew J. Triebe,

John W. SutherlandAuthors Info & Claims

Journal of Intelligent Manufacturing, Volume 31, Issue 5

Pages 1175 - 1189

https://doi.org/10.1007/s10845-019-01504-w

Published: 01 June 2020 Publication History

Abstract

Tool wear is one of the consequences of a machining process. Excessive tool wear can lead to poor surface finish, and result in a defective product. It can also lead to premature tool failure, and may result in process downtime and damaged components. With this in mind, it has long been desired to monitor tool wear/tool condition. Kernel principal component analysis (KPCA) is proposed as an effective and efficient method for monitoring the tool condition in a machining process. The KPCA-based method may be used to identify faults (abnormalities) in a process through the fusion of multi-sensor signals. The method employs a control chart monitoring approach that uses Hotelling’s T²-statistic and Q-statistic to identify the faults in conjunction with control limits, which are computed by kernel density estimation (KDE). KDE is a non-parametric technique to approximate a probability density function. Four performance metrics, abnormality detection rate, false detection rate, detection delay, and prediction accuracy, are employed to test the reliability of the monitoring system and are used to compare the KPCA-based method with PCA-based method. Application of the proposed monitoring system to experimental data shows that the KPCA based method can effectively monitor the tool wear.

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Information

Published In

cover image Journal of Intelligent Manufacturing

Journal of Intelligent Manufacturing Volume 31, Issue 5

Jun 2020

223 pages

ISSN:0956-5515

Issue’s Table of Contents

© Springer Science+Business Media, LLC, part of Springer Nature 2019.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 June 2020

Accepted: 14 October 2019

Received: 19 April 2019

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Indiana Next Generation Manufacturing Competitiveness Center
National Science Foundation
Wabash Heartland Innovation Network

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