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Real-time monitoring of high-power disk laser welding statuses based on deep learning framework

Author

Listed:
  • Yanxi Zhang

    (Guangdong University of Technology)

  • Deyong You

    (Guangdong University of Technology)

  • Xiangdong Gao

    (Guangdong University of Technology)

  • Congyi Wang

    (Guangdong University of Technology)

  • Yangjin Li

    (Guangdong University of Technology)

  • Perry P. Gao

    (US-China Youth Education Solutions Foundation)

Abstract

The laser welding quality is determined by its welding statuses, and online welding statuses are depicted by the real-time signals captured from the welding process. A multiple-sensor system is designed to obtain information as comprehensive as possible for welding statuses monitoring. The multiple-sensor system includes an auxiliary illumination visual sensor system, an ultraviolet and visible band visual sensor system, a spectrometer and two photodiodes. The signals captured by different sensors are analyzed via signal or digital image processing algorithms, and distinct features are extracted from these signals to depict the online welding statuses. A deep learning framework based on stacked sparse autoencoder (SSAE) is established to model the relationship between the multi-sensor features and their corresponding welding statuses, and Genetic algorithm (GA) is applied to optimize the parameters of the SSAE framework (SSAE-GA). The proposed framework achieves higher accuracy and stronger robustness in monitoring welding status by comparing with the backpropagation neural network, support vector machine and random forest. Three new experiments with different welding parameters are implemented to validate the effectiveness and generalization of our proposed method. This study provides a novel and accurate method for high-power disk laser welding status monitoring.

Suggested Citation

  • Yanxi Zhang & Deyong You & Xiangdong Gao & Congyi Wang & Yangjin Li & Perry P. Gao, 2020. "Real-time monitoring of high-power disk laser welding statuses based on deep learning framework," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 799-814, April.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:4:d:10.1007_s10845-019-01477-w
    DOI: 10.1007/s10845-019-01477-w
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    References listed on IDEAS

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    1. Changqing Liu & Yingguang Li & Guanyan Zhou & Weiming Shen, 2018. "A sensor fusion and support vector machine based approach for recognition of complex machining conditions," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1739-1752, December.
    2. Chung-Feng Jeffrey Kuo & Chun-Ping Tung & Wei-Han Weng, 2019. "Applying the support vector machine with optimal parameter design into an automatic inspection system for classifying micro-defects on surfaces of light-emitting diode chips," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 727-741, February.
    3. D. Yu. Pimenov & A. Bustillo & T. Mikolajczyk, 2018. "Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 1045-1061, June.
    4. Tamilselvan, Prasanna & Wang, Pingfeng, 2013. "Failure diagnosis using deep belief learning based health state classification," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 124-135.
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    Cited by:

    1. Dongxiang Hou & Xiaodong Wang & Qing Song & Xuesong Mei & Haicheng Wang, 2024. "A quality improvement method for complex component fine manufacturing based on terminal laser beam deflection compensation," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 331-341, January.
    2. Xingguo Wang & Tianyun Chen & Yiming Wang & Dongliang Zheng & Xiaoyu Chen & Zhuang Zhao, 2023. "The 3D narrow butt weld seam detection system based on the binocular consistency correction," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2321-2332, June.

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