SEM images data set development of ML models to predict hydrogen embrittlement in welded 316 L/304 L stainless steels

This study focuses on the machine learning (ML)-based identification of defects caused by hydrogen embrittlement (H2E) in the welded zones of 316 L/304 L stainless steels. It involves developing a robust SEM image dataset to train ML models for accurate defect identification. Initially, Gas Metal Arc Welding (GMAW) samples were manufactured with weld gap variations of 0.8 mm, 1.2 mm, and 1.5 mm. The welding parameters used were: (i) welding speeds of 15 mm/sec and 10 mm/sec, (ii) wire feed rate of 5.5 m/min, and (iii) voltage of 15.5 V. The samples were then exposed to a hydrogen gas environment at a pressure of 80 bar for 150 h. When analyzed using scanning electron microscopy (SEM) & electron backscatter diffraction (EBSD), H2E was observed on the surfaces of the welded zones (WZ) and heat-affected zones (HAZ). These defects, validated through literature, were segregated / sectioned as defect-based feature images and stored as a dataset. A preliminary analysis of the images validated after with 16 DOE's using AlexNet, a convolutional neural network (CNN)-based ML model, showed significant identification of these defects with 90 % accuracy. The trained models helped identify areas and understand previously unidentified defects. Through a focused discussion on defect detection, supported by validation using classification (CNN Accuracy, Precision, Recall, and F1-Score) and regression metrics (R2 and Success Rate), the article demonstrates the potential of ML-based approaches in advancing welding diagnostics.