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

  • Yanxi Zhang
  • Deyong You
  • Xiangdong GaoEmail author
  • Congyi Wang
  • Yangjin Li
  • Perry P. Gao
Article

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.

Keywords

Features fusion Deep learning Genetic algorithm Stacked sparse autoencoder Multiple-sensor signals 

Notes

Acknowledgements

This work was partly supported by the National Natural Science Foundation of China (Grant Numbers 51675104, 61703110), Innovation Team Project, Department of Education of Guangdong Province, China (2017KCXTD010), the Guangdong Provincial Natural Science Foundation of China (Grant Numbers 2017A030310494, 2016A030310347) and Youth Science Foundation of Guangdong University of Technology (Grant Number 16ZK0010). Many thanks are given to Katayama Laboratory of Osaka University for their assistance of laser welding experiments.

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Copyright information

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

Authors and Affiliations

  • Yanxi Zhang
    • 1
  • Deyong You
    • 1
  • Xiangdong Gao
    • 1
    Email author
  • Congyi Wang
    • 1
  • Yangjin Li
    • 1
  • Perry P. Gao
    • 2
  1. 1.Guangdong Provincial Welding Engineering Technology Research CenterGuangdong University of TechnologyGuangzhouChina
  2. 2.US-China Youth Education Solutions FoundationNew YorkUSA

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