Xiao-chen Wang

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

A prediction method based on particle swarm optimisation (PSO) and a one-dimensional convolutional neural network (1D-CNN) was developed and integrated into an online system that enables single tube wall thickness prediction, sawing parameter calculation, and batch visualisation for process adjustment and sawing decisions.