APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES FOR ERROR PREVENTION AND QUALITY CONTROL IN MIG/MAG WELDING PROCESSES: A COMPREHENSIVE REVIEW

Abstract

Welding techniques, particularly MIG/MAG (Metal Inert Gas/Metal Active Gas) processes, are essential for various industries, including automotive, construction, aerospace, and defense. Ensuring the quality and accuracy of welds is crucial for efficient production and product reliability. However, numerous issues can arise during MIG/MAG welding, often resulting in suboptimal quality. This study presents a comprehensive review of research efforts employing AI techniques to address errors and defects in MIG/MAG welding. The review systematically analyzes relevant studies published since 2018, focusing on three key aspects: datasets employed, methodologies and approaches adopted, and performance metrics reported. The findings reveal a significant adoption of both machine learning and deep learning techniques, with the choice of approach dependent on factors such as the nature of input data, 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 tasks like image-based defect detection and time-series analysis for quality prediction. However, traditional machine learning algorithms have also been utilized, often in conjunction with dimensionality reduction or feature selection techniques. The review highlights the diverse range of performance metrics employed, including accuracy, precision, recall, F1-score, mean squared error (MSE), and root mean squared error (RMSE), with the selection contingent upon the specific task (classification or regression) and desired trade-off between different performance aspects. While many studies have reported promising results, with accuracy rates frequently exceeding 90%, challenges remain in real-world industrial settings, where factors such as noise, occlusions, and rapidly changing welding conditions can pose significant hurdles. This review serves as a comprehensive guide for researchers and practitioners working in the field of AI-assisted error prevention and quality control for MIG/MAG welding processes, highlighting current trends, methodologies, and future research directions.