  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 leverage state-of-the-art machine learning
models to enhance the transformation of on-line measurements into significantly
a more accurate Ra surface roughness metric. By comparing a selection of
data-driven approaches, including both deep learning and non-deep learning
methods, to the close-form transformation, we evaluate their potential for
improving surface texture control in temper strip steel manufacturing.
