Automated weld defect detection using an optimized AlexNet–GRU model with Greylag Goose optimization algorithm

Weld component durability is essential in industrial production processes, where unidentified defects can cause serious operational failures. Automated weld flaw identification through deep learning technologies provides a dependable solution that eliminates the need for human inspection. This research introduces a new deep learning framework that integrates AlexNet with Gated Recurrent Unit (GRU) Network and Greylag Goose Optimization (GGO) method to achieve effective hyperparameter tuning. The proposed framework tests its performance through the RIAWELC dataset which contains 24,407 weld radiograph images divided into four categories: porosity, crack, lack of penetration, and no defect. The weld images receive quality improvement through advanced preprocessing techniques, which use median filtering, Contrast Limited Adaptive Histogram Equalization, and data augmentation methods. AlexNet extracts discriminative spatial features from visual data while GRU learns the contextual relationships among these features. The GGO technique enables the suggested framework to achieve its optimal performance through the GGO method which optimizes critical hyperparameters such as learning rate, dropout, batch size, kernel size, and GRU hidden units. The experimental results demonstrate that the suggested framework outperforms all existing methods by achieving 99.3% accuracy in binary classification and 98.9% average accuracy in multi-class classification.