Prevention of defects during radial-axial rolling of rings based on online data analysis

• Applicant: Professor Dr.-Ing. Bernd Kuhlenkötter
• Subject Area: Primary Shaping and Reshaping Technology, Additive Manufacturing
• Term: since 2018
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 404517758

Seamless, ring-shaped components with requirement profiles such as high dynamic load capacity and high variability are needed for many machines and systems in all branches of industry. Radial-axial ring rolling (RARR) is the most important process for the manufacture of such components and enables the production of rings in the order of 100 mm up to 16 m outer diameter, up to 4 m ring height and a component weight of up to 300 t. Within the completed project on " Fehlervermeidung in Radial-Axial Ringwalzprozessen durch Online-Analyse der Zustandsdaten ", machine learning models were trained and used to predict various shape defects (non-circularity and waviness). This prediction is done in a first step following the process and thus off-line, but is also done on-line during the process in the course of the project. Such on-line prediction can be used to optimize the rolling process to the extent that shape defects, which at the time of rolling are detectable and learnable by machine learning models through patterns in the rolling data that cannot be interpreted by humans, can be prevented instead of merely predicted. The previous work focuses on a classification of the available ring data and will be further extended to a regression problem. By such a regression approach, occurring shape defects can not only be classified, but also predicted in their degree of expression ("1.212 mm out-of-roundness"). Regression enables a better implementation of the control of the rolling process, since the intervention strength of suitable counter-rolling measures can be better estimated. In addition to this, the previous research project identified a research gap in the use of semi-supervised learning data and synthetic data generation in RARR. For this reason, within the continuation application, precisely these research fields are to be dedicated on the basis of the data and findings obtained so far. In order to further increase the quality of the research, additional data from industrial rolling mills at cooperating ring rolling companies (thyssenkrupp rothe erde Germany GmbH and Schmiedewerke Gröditz) are to be included. To cover even small ring mill dimensions, extensive rolling will also be carried out on the institute's own ring rolling mill. The industrial relevance of the proposed topic is highlighted by the attached letters of intent from thyssenkrupp rothe erde Germany GmbH and Schmiedewerke Gröditz. The basic research relevance has been demonstrated within the previous project by numerous publications of the results and research gaps.

• DFG Programme: Research Grants