Role of Machine Learning and Big Data Analytics in Enhancing the Efficiency of Gas Metal Arc Welding Process

Abstract

Gas Metal Arc Welding is a widely used process in manufacturing due to its versatility, speed, and cost-effectiveness. However, variations in process parameters can lead to quality issues affecting production efficiency and increasing rework costs. Optimising these parameters is essential to ensure high-quality welds, reduce material waste and improve overall manufacturing productivity. Traditional optimisation models are often limited by specific welding conditions and constrained experimental data. This research employs machine learning and big data analytics to develop a generalised optimisation model for Gas Metal Arc Welding, leveraging literature data to overcome experimental limitations. Data preprocessing, including imputation for missing values, was applied to enhance data quality. Supervised machine learning algorithms were compared for predicting key weld characteristics, including tensile strength, penetration, and weld width. A Medium Gaussian SVM model predicted weld tensile strength with 85.60% accuracy, while an interactions linear model achieved 80.40% accuracy for weld penetration. A stepwise linear regression model provided 95.80% accuracy for weld width prediction. By optimising machine learning models for different manufacturing scenarios, this study offers a data-driven approach to parameter selection, improving weld quality and operational efficiency. The findings bridge machine learning-based welding optimisation and industrial applications, supporting data-driven decision-making for enhanced production performance.