Steel Surface Roughness Parameter Calculations Using Lasers and Machine Learning Models

TL;DR

This paper explores the use of machine learning models to improve the accuracy of online surface roughness measurements in steel manufacturing, enabling better quality control and real-time process adjustments.

Contribution

It introduces a comparison of machine learning approaches, including deep learning, for transforming online measurements into precise surface roughness metrics.

Findings

Machine learning models outperform traditional transformation methods.

Deep learning approaches provide the highest accuracy in roughness estimation.

Enhanced measurement accuracy supports real-time process control in steel production.

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 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.