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

Author

Listed:
  • Wo Jae Lee

    (Purdue University)

  • Gamini P. Mendis

    (Purdue University)

  • Matthew J. Triebe

    (Purdue University)

  • John W. Sutherland

    (Purdue University)

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.

Suggested Citation

  • Wo Jae Lee & Gamini P. Mendis & Matthew J. Triebe & John W. Sutherland, 2020. "Monitoring of a machining process using kernel principal component analysis and kernel density estimation," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1175-1189, June.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:5:d:10.1007_s10845-019-01504-w
    DOI: 10.1007/s10845-019-01504-w
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    References listed on IDEAS

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    1. Guofeng Wang & Yanchao Zhang & Chang Liu & Qinglu Xie & Yonggang Xu, 2019. "A new tool wear monitoring method based on multi-scale PCA," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 113-122, January.
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    Citations

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    Cited by:

    1. Youngju Kim & Hoyeop Lee & Chang Ouk Kim, 2023. "A variational autoencoder for a semiconductor fault detection model robust to process drift due to incomplete maintenance," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 529-540, February.
    2. Faping Zhang & Jialun Zhang & Junjiu Ma, 2023. "Data-manifold-based monitoring and anomaly diagnosis for manufacturing process," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 3159-3177, October.
    3. Xuejun Zhao & Yong Qin & Changbo He & Limin Jia, 2022. "Underdetermined blind source extraction of early vehicle bearing faults based on EMD and kernelized correlation maximization," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 185-201, January.
    4. Yanning Sun & Wei Qin & Zilong Zhuang & Hongwei Xu, 2021. "An adaptive fault detection and root-cause analysis scheme for complex industrial processes using moving window KPCA and information geometric causal inference," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 2007-2021, October.
    5. Jinping Liu & Jie Wang & Xianfeng Liu & Tianyu Ma & Zhaohui Tang, 2022. "MWRSPCA: online fault monitoring based on moving window recursive sparse principal component analysis," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1255-1271, June.
    6. Sachin Kumar & T. Gopi & N. Harikeerthana & Munish Kumar Gupta & Vidit Gaur & Grzegorz M. Krolczyk & ChuanSong Wu, 2023. "Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 21-55, January.
    7. Li Duan & Jingxian Zhou & You Wu & Wenyao Xu, 2022. "A novel and highly efficient botnet detection algorithm based on network traffic analysis of smart systems," International Journal of Distributed Sensor Networks, , vol. 18(3), pages 15501477211, March.
    8. Changyuan Yang & Sai Ma & Qinkai Han, 2023. "Unified discriminant manifold learning for rotating machinery fault diagnosis," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3483-3494, December.

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