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

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 m...

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Bibliographic Details
Published in:Journal of intelligent manufacturing 2020-06, Vol.31 (5), p.1175-1189
Main Authors: Lee, Wo Jae, Mendis, Gamini P., Triebe, Matthew J., Sutherland, John W.
Format: Article
Language:English
Subjects:
Abnormalities
Advanced manufacturing technologies
Business and Management
Control
Control charts
Control limits
Defective products
Downtime
Fault detection
Kernels
Machinery condition monitoring
Machines
Machining
Manufacturing
Mechatronics
Monitoring methods
Monitoring systems
Performance measurement
Principal components analysis
Probability density functions
Processes
Production
Robotics
Signal processing
Statistical analysis
Surface finish
Tool wear
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Summary: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 T 2 -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.
ISSN:0956-5515
1572-8145
DOI:10.1007/s10845-019-01504-w