Automated Welding Defect Recognition through Deep Learning Fusion: CNN and SVM Integration

The research is briefly summarized in the abstract, which includes metrics and important conclusions from the assessment of the CNN-SVM hybrid model for welding defect classification. The model performs exceptionally well in terms of precision, recall, and F1-score metrics. It routinely outperforms 96% in terms of all defect classes, including Ideal Weld, Cracks, Porosity, Undercut, and Slag Inclusion. These measures demonstrate the model’s strong capability to detect welding flaws, which is an essential component for guaranteeing structural integrity in a range of industrial settings. The model’s good generalization skills are attributed to the support proportions, which show a balanced representation of fault classes within the dataset. The method was able to classify various welding flaws with an amazing overall accuracy of 96.92% by utilizing a dataset of 4,880 pictures. The model’s remarkably high total accuracy of 96.93% demonstrates its dependability. Additionally, the abstract makes use of macro, weighted, and micro averages to demonstrate the proficiency of the model and provide a comprehensive understanding of its performance. A more nuanced evaluation is provided by the weighted average, which considers the support proportions, while the macro average treats all classes equally. The micro average provides a thorough assessment of the model’s overall performance by combining data from all classes. This study represents the significant advancement in automated welding fault identification with a robust system that satisfies the accuracy or dependability criteria of industrial settings. The abstract provides a concise overview of the work, emphasizing the model’s precision, applicability, and potential impact on enhancing welding inspection protocols across diverse industrial contexts.