Quantitative detection of pipeline weld defects in metal magnetic memory based on two-stream CNN‐GS‐MLSSVR

Welds are critical pipe connections, where weld defects can create stress concentrations and pose significant safety risks. The metal magnetic memory (MMM) method is widely employed for detecting pipeline weld defects. In this paper, the relationship between weld defects of different sizes and MMM signals is simulated by numerical simulation. The results show that the MMM signal of defects changes in a regular fashion with the defect size. Subsequently, the correctness of this numerical simulation conclusion is verified by experiments. A method based on the combination of a two-stream convolutional neural network (TSCNN) and a grid search (GS)‐optimised multi‐output least-squares support vector regression (MLSSVR) for welding defect size prediction is proposed to address challenges such as quantifying weld defects, the limited number of detection samples and the complexity of manual feature extraction. This method improves the quantification accuracy and reduces the computation time compared to traditional machine learning and provides a new method for welding defect quantification.