Use of machine learning methods for predicting the Remaining-Useful-Life of tools using the example of mandrel rolls in radial-axial ring rolling

• Applicant: Professor Dr.-Ing. Bernd Kuhlenkötter
• Subject Area: Primary Shaping and Reshaping Technology, Additive Manufacturing
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 464881255

For many machines and plants in all branches of industry, seamless ring-shaped components with high requirement specifications, such as high dynamic load capacity and high variability, are required. For the production of such components, radial-axial ring rolling (RARR) is the most important process. With RARR, rings of 100 mm up to 16 m in outer diameter, up to 4 m in ring height and component weights of up to 300 t can be produced. Despite a long research history in ring rolling with many successes, there is still a need for research in the field of predicting mandrel roll fracture. At present, mandrel roll breakage occurs unpredictably and without a directly identifiable cause and can occur up to once per shift, depending on the ring-rolling machine and capacity utilisation. Mandrel roll breakage leads to production downtime, defective rings and unplanned maintenance work, resulting in increased production time and rising costs. As a large number of influencing factors and the non-linear dependencies between them preclude the use of proven investigations to determine qualitative and quantitative influences, as shown in a study by thyssenkrupp Rothe Erde (tkRE) referred to in the preparatory work, machine learning (ML) is used to address this problem. The multidimensional nature of the problem and the non-linear influences make this method particularly suitable, since current algorithms, especially those from the field of deep neural networks, can demonstrate significant success in solving complex problems in a wide range of disciplines. However, the use of ML also creates new challenges, since without a suitable database the use of ML fails. At the same time, data acquisition without domain-specific knowledge is not effective. In this research project, therefore, a plant-independent data acquisition concept for the use of data-driven analysis methods on the one hand, and the foundations for a Remaining-Useful-Life (RUL) model of the mandrel roll in RARR using ML on the other hand are being developed. In order to sufficiently elaborate these two goals, first of all an analysis of the influencing variables for the mandrel roll fracture is carried out. Subsequently, a sensor concept will be developed and extensive sensor technology will be installed at a production plant with a sufficiently large and variable production volume. For a prototypical implementation, the ring roll machine of the Chair of Production Systems can be used within the project, whereby the actual data acquisition is carried out at two industrial companies (tkRE, Schmiedewerke Gröditz). This guarantees a wide range of rolling operations. The data recorded in this way are processed syntactically and semantically and evaluated by means of machine learning methods, so that in the end a regression model for predicting the RUL of a mandrel roll is validated.

• DFG Programme: Research Grants