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Prediction of high power laser welding status based on PCA and SVM classification of multiple sensors

Author

Listed:
  • Guiqian Liu

    (Guangdong University of Technology)

  • Xiangdong Gao

    (Guangdong University of Technology)

  • Deyong You

    (Guangdong University of Technology)

  • Nanfeng Zhang

    (Guangdong University of Technology)

Abstract

In order to explore the relationship between the welding process and welded quality, a multiple sensor fusion system was built to obtain the photodiode and visible light information during the welding. Features of keyhole, plasma and spatters were extracted from five sensors, including two photodiode sensors, one spectrometer sensor, one ultraviolet and visible light sensing camera and one auxiliary illumination sensing camera, 15 features were analyzed by normalization and principle component analysis, and principle component numbers was chosen as input parameters of support vector machine classification, Three weld quality types were defined according to the weld seam width and weld depth. The overall accuracy of training data was 98%, and the overall accuracy of testing data was 91%, respectively. Experimental results showed that the estimation on welding status was accurate and effective, thus providing an experimental example of monitoring high-power disk laser welding quality.

Suggested Citation

  • Guiqian Liu & Xiangdong Gao & Deyong You & Nanfeng Zhang, 2019. "Prediction of high power laser welding status based on PCA and SVM classification of multiple sensors," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 821-832, February.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:2:d:10.1007_s10845-016-1286-y
    DOI: 10.1007/s10845-016-1286-y
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    References listed on IDEAS

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    1. Kuanfang He & Xuejun Li, 2016. "A quantitative estimation technique for welding quality using local mean decomposition and support vector machine," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 525-533, June.
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    Cited by:

    1. Alexander Gerling & Holger Ziekow & Andreas Hess & Ulf Schreier & Christian Seiffer & Djaffar Ould Abdeslam, 2022. "Comparison of algorithms for error prediction in manufacturing with automl and a cost-based metric," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 555-573, February.
    2. Alon Ratner & Michael Wood & Maximilian Chowanietz & Nikhil Kumar & Rashik Patel & Paul Hadlum & Abhishek Das & Iain Masters, 2022. "Laser Doppler Vibrometry for Evaluating the Quality of Welds in Lithium-Ion Supercells," Energies, MDPI, vol. 15(12), pages 1-20, June.
    3. Carlos Gonzalez-Val & Adrian Pallas & Veronica Panadeiro & Alvaro Rodriguez, 2020. "A convolutional approach to quality monitoring for laser manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 789-795, March.
    4. Anshuman Kumar Sahu & Siba Sankar Mahapatra, 2021. "Prediction and optimization of performance measures in electrical discharge machining using rapid prototyping tool electrodes," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2125-2145, December.
    5. Zhenyu Liu & Donghao Zhang & Weiqiang Jia & Xianke Lin & Hui Liu, 2020. "An adversarial bidirectional serial–parallel LSTM-based QTD framework for product quality prediction," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1511-1529, August.
    6. Roham Sadeghi Tabar & Kristina Wärmefjord & Rikard Söderberg & Lars Lindkvist, 2021. "Critical joint identification for efficient sequencing," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 769-780, March.
    7. Runquan Xiao & Yanling Xu & Zhen Hou & Chao Chen & Shanben Chen, 2022. "An automatic calibration algorithm for laser vision sensor in robotic autonomous welding system," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1419-1432, June.

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