Steel Surface Roughness Parameter Calculations Using Lasers and Machine Learning Models
This work addresses the need for precise, real-time quality control in steel manufacturing to ensure consistent product quality and enable closed-loop process adjustments, representing an incremental improvement over existing on-line techniques.
The study tackled the problem of accurately measuring steel surface roughness in real-time during manufacturing by using machine learning models to transform on-line laser measurements into Ra surface roughness metrics, resulting in significantly improved accuracy compared to traditional methods.
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.