Research on gap-adaptive high frequency pulse arc welding of stainless-steel thin plate based on machine learning

Abstract

The demand for butt welding of stainless-steel thin plates has been increasing year by year in industries such as shipbuilding, pharmaceuticals, petrochemicals, and food processing. However, during the butt welding of thin plates, the low structural rigidity causes real-time change in the gap, which complicates the ability of automated welding to achieve uniform weld seam geometry. Therefore, this paper aims to overcome the challenges of cross coupling and the sensitivity of hyperparameter selection and realize the gap-adaptive welding during the high frequency pulse arc welding process by implementing to optimize the number of trees and the minimum number of leaf nodes in the random forest model. After the optimal welding parameters are then used to train the random forest model, a gap-adaptive control platform, which take gap, weld face width, weld root width, and face reinforcement as input features and peak current, wire feed speed, and welding speed as output features, is established to enable real-time measure the gap by using a laser track sensor. The results demonstrate that under fixed and gradient gap conditions, the weld seam geometry is uniformly formed and no significant defects can be found. Under step gap conditions, gap-adaptive welding effectively prevents burn-through defects and ensures stable weld seam geometry. Furthermore, microstructural and mechanical property characterization indicates that an increase in frequency during welding effectively refines the grain size in the seam. The tensile strength values of the samples are similar and all samples fracture at the base metal.