Transformation of a rolling mill aggregate to a cyber physical production system: from sensor retrofitting to machine learning

Publikationen: Beitrag in FachzeitschriftArtikelForschung(peer-reviewed)



This paper describes the transformation of a rolling mill aggregate from a stand-alone solution to a fully integrated cyber
physical production system. Within this process, already existing load cells were substituted and additional inductive and
magnetic displacement sensors were applied. After calibration, those were fully integrated into a six-layer digitalization
architecture at the Smart Forming Lab at the Chair of Metal Forming (Montanuniversitaet Leoben). Within this framework,
two front end human machine interfaces were designed, where the first one serves as a conditionmonitoring system during the
rolling process. The second user interface visualizes the result of a resilient machine learning algorithm, which was designed
using Python and is not just able to predict and adapt the resulting rolling schedule of a defined metal sheet, but also to learn
from additional rolling mill schedules carried out. This algorithm was created on the basis of a black box approach, using data
from more than 1900 milling steps with varying roll gap height, sheet width and friction conditions. As a result, the developed
program is able to interpolate and extrapolate between these parameters as well as different initial sheet thicknesses, serving
as a digital twin for data-based recommendations on schedule changes between different rolling process steps. Furthermore,
via the second user interface, it is possible to visualize the influence of this parameters on the result of the milling process.
As the whole layer system runs on an internal server at the university, students and other interested parties are able to access
the visualization and can therefore use the environment to deepen their knowledge within the characteristics and influence
of the sheet metal rolling process as well as data science and especially fundamentals of machine learning. This algorithm
also serves as a basis for further integration of materials science based data for the prediction of the influence of different
materials on the rolling result. To do so, the rolled specimens were also analyzed regarding the influence of the plastic strain
path on their mechanical properties, including anisotropy and materials’ strength.


FachzeitschriftJournal of Intelligent Manufacturing
Frühes Online-Datum24 Okt 2021
StatusVeröffentlicht - 24 Okt 2021