Steel surface roughness parameter prediction from laser reflection data using machine learning models

Abstract Control of surface texture in strip steel is essential to meet customer requirements during galvanizing and temper rolling processes. Traditional methods rely on post-production stylus measurements, while on-line techniques offer non-contact and real-time measurements of the entire strip. However, ensuring accurate measurement is imperative for their effective utilization in the manufacturing pipeline. Moreover, accurate on-line measurements enable real-time adjustments of manufacturing processing parameters during production, ensuring consistent quality and the possibility of closed-loop control of the temper mill. In this study, we formulate the manufacturing issue into a Time Series Extrinsic Regression problem and a Machine Vission problem and leverage state-of-the-art machine learning models to enhance the transformation of on-line measurements into a significantly more accurate Ra surface roughness metric. By comparing a selection of data-driven approaches, including both deep learning such as convolutional, recurrent, and transformer networks and non-deep learning methods such as Rocket and XGBoost, to the close-form transformation, we evaluate their potential using Root Mean Squared Error (RMSE) and correlation for improving surface texture control in temper strip steel manufacturing.