Abstract
This study presents a comprehensive review of research applying artificial intelligence (AI) techniques to prevent defects in arc welding processes. Arc welding is essential across various industries, but numerous issues can arise, impacting weld quality and production efficiency. The review systematically analyzes relevant studies published since 2018, focusing on three key aspects: datasets used, methodologies and approaches adopted, and performance metrics reported. The findings reveal significant adoption of both machine learning and deep learning techniques, with the choice depending on factors like input data nature, welding process dynamics, and computational requirements. Deep learning models, particularly convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, have demonstrated superior performance in image-based defect detection and time-series analysis for quality prediction. However, traditional machine learning algorithms have also been utilized, often coupled with dimensionality reduction or feature selection techniques. The review highlights the diverse range of performance metrics employed, such as accuracy, precision, recall, F1-score, mean squared error (MSE), and root mean squared error (RMSE). Metric selection depends on the specific task (classification or regression) and the desired trade-off between different performance aspects. While many studies reported promising results with accuracy rates frequently exceeding 90%, challenges remain in real-world industrial settings due to factors like noise, occlusions, and rapidly changing welding conditions. This review serves as a comprehensive guide for researchers and practitioners in AI-assisted defect prevention and quality control for arc welding processes, highlighting current trends, methodologies, and future research directions.
Published Version
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