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

Yanxi Zhang (), Deyong You, Xiangdong Gao (), Congyi Wang, Yangjin Li and Perry P. Gao
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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

Journal of Intelligent Manufacturing, 2020, vol. 31, issue 4, No 1, 799-814

Abstract: 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 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10845-019-01477-w

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