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

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.