Joint project Roughness control

  Industrial rolling mill with duo rolling frame Copyright: © Dr. Martin Riedel

Feedback control of surface properties of flat-rolled semi-finished products by development of a novel control concept based on online-roughness measurements of the strip surface


Key Info

Basic Information

01.01.2020 to 31.12.2023
Roughness control
Production Systems



Christopher Schulte

Industrial Systems Group Manager


+49 241 80-28013




Logo DFG Copyright: © DFG

Various technological fields, such as telecommunications or high-frequency technology, utilize thin, metallic strips. These strips have a surface roughness that significantly affects important surface properties such as paintability, called adhesion of paint, surface gloss, or the tribological system, called coefficient of friction, which have a decisive impact on the functionality and appearance of the products. Therefore, optimal control of surface properties is an important factor for quality and performance. Our current research focuses on improving this control. We are collaborating with the Institute for Metal Forming of RWTH Aachen University to examine approaches to regulate these product properties using control engineering methods and state-of-the-art roughness measurement.


Project Goals and Methods

The aim of the project is to achieve independent control of selected surface properties while maintaining a constant strip geometry. To attain this, a first model-based roughness control is implemented, and the controllability proof regarding the strip properties of tribology, paint adhesion, and surface gloss will be proved. In the second phase of the project, the rerolling process will be designed for the targeted adjustment of these surface properties. The objective is to validate an extended control loop, including soft sensors, which, based on the previously derived embossing model, now allows for direct tracking of surface properties.

The control loop will be expanded to include target selection, and additional models will be integrated into the existing model predictive control to adjust the additional control variables. To achieve this, the strip will be processed in two consecutive rolling passes, each with optimized settings. Furthermore, data will be continuously recorded, evaluated in real-time, and used to improve the models to detect parameter variations that affect product quality.



The research in the field of rolling aims to investigate methods and identify pathways that enable the direct control of surface properties, such as the gloss of metallic strips. By online data acquisition and evaluation, it will be possible to learn machine and process parameters in real-time and feed them to an adaptive control system. In combination with expert knowledge about the process, the appropriate conclusions can be drawn from the data, thereby achieving the transition from pure machine control to quality control.


Project partner


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