Real time Monitoring Method of Welding Defects Based on LSTM Sequence Model

During laser welding, abnormal conditions can result in workpiece defects and product failure. Therefore, a real-time monitoring system is required to detect laser welding processes and ensure product quality. In this study, we developed a real-time laser welding data acquisition system to collect plasma density, laser intensity, and molten pool temperature data during the welding process. Additionally, we established a neural network based on a combination of LSTM and CNN models to rapidly detect laser welding defects. The experimental results demonstrate that this method can effectively identify welding defects with an average accuracy rate of 96%. Furthermore, the LSTM layer can significantly improve the prediction performance of the CNN feature extraction model, particularly for forecasting and non-local features such as sequence offsets and delays.