Research on Welding Parameter Optimization and Automatic Control Based on Machine Learning Algorithm

With the continuous improvement of welding technology requirements in the manufacturing industry, automation and parameter optimization of the welding process have become increasingly important. This article studies the optimization and automation control method of welding parameters based on ML (Machine Learning) algorithm, aiming to achieve high-quality, efficient, and highly automated welding processes. By analyzing and processing a large amount of welding data, we have developed a series of prediction models for real-time prediction and adjustment of welding parameters, such as current, voltage, and welding speed, to ensure welding quality. In addition, we have proposed an ML based automation control strategy that can automatically adjust and control the welding process in actual production. We used various ML algorithms, including regression analysis, neural networks, and support vector machines, to establish predictive models for welding parameters. In addition, we also studied the automation control methods of the welding process and proposed an ML based automation control strategy. This strategy uses a prediction model as input, automatically adjusting welding parameters and equipment settings based on real-time welding status and prediction results to achieve the best welding effect.