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.

翻译：控制带钢表面纹理对满足镀锌和调质轧制过程中的客户需求至关重要。传统方法依赖于生产后的触针式测量，而在线技术则能实现整个带钢的非接触式实时测量。然而，确保测量精度对于其在制造流程中的有效应用至关重要。此外，精确的在线测量可在生产过程中实现制造工艺参数的实时调整，从而保证质量一致性并为调质轧机提供闭环控制的可能性。在本研究中，我们利用最先进的机器学习模型，将在线测量数据转化为更准确的Ra表面粗糙度指标。通过比较多种数据驱动方法（包括深度学习和非深度学习方法）与闭式变换的差异，我们评估了这些方法在改善调质带钢制造中表面纹理控制方面的潜力。