Data-Driven Detection of Laser Welding Defects Based on Real-Time Spectrometer Signals

The spectrometer is applied in this paper to get the in-process information of welding defects during the high-power disk laser welding process. The high-dimensional signal captured by the spectrometer is fed into a data-driven framework based on stacked auto-encoder (SAE) to automatically extract salient features for performing real-time welding defects detection. The particle swarm optimization (PSO) algorithm is employed to optimize the proposed data-driven framework by acquiring the global optimal parameters to strengthen its capability in extracting the representative features from the original high-dimensional signal. The extracted features are classified by a softmax classifier to get the real-time identifications of the welding defects. The proposed framework is compared with the conventional shallow artificial intelligent methods, such as back-propagation (BP) neural network and support vector machine (SVM), and reveals better performance. The actual welding experiments under different welding parameters are implemented to validate the detection accuracy of our proposed data-driven framework. This paper provides an effective framework for the detection of the high-power disk laser welding status in real-time.