Interpretable machine learning for high-precision wall thickness prediction of hot-rolled seamless steel tubes

Abstract

To improve the accuracy of axial wall thickness prediction and reduce reliance on manual measurements in hot-rolled steel tube production, a prediction method based on particle swarm optimisation (PSO) and a one-dimensional convolutional neural network (1D-CNN) was developed. A dataset was constructed from 131 industrial samples collected from the production line. An input variable set was established to characterise entry wall thickness, geometric and overall deformation indicators, pass schedule profile features, speed schedule profile and thermal conditions. PSO was then employed to optimise the key hyperparameters of the 1D-CNN. The proposed model was compared with several machine learning models. The results showed that the PSO-1D-CNN model achieved the best predictive performance, with a test root mean square error of 0.0281, a mean absolute error of 0.0217, and a coefficient of determination R² of 0.913. Further interpretability analysis using SHapley Additive exPlanations revealed that the centroid of the reduction distribution, the number of stands, the diameter-to-wall ratio, the tube temperature at the sizing exit, and the radial compression ratio were the most influential variables affecting the predictions. Finally, the proposed model was integrated into an online system. This system enables single tube wall thickness prediction, sawing parameter calculation, and batch visualisation for process adjustment and sawing decisions.