AI predicts material wear
Wear causes considerable economic losses. Dr. Stefanie Hanke has developed an AI method to reliably predict material wear.
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Factors such as friction, wear and material fatigue cause major economic losses in day-to-day production. Due to the complexity and varying degrees of utilization of industrial plants, it is difficult to predict exactly when components need to be replaced. Dr. Stefanie Hanke, Professor of Materials Engineering at the Faculty of Engineering at the University of Duisburg-Essen, is working on an AI-based method to precisely predict material wear.
In her study, Hanke is investigating how to better classify wear characteristics in materials. She is relying on artificial intelligence (AI) because traditional models often fail in this area. "We first collect data on the forces acting on the parts and then analyze them under an electron microscope, which shows us the surface damage in detail. At the end, we train an AI model with this data to predict the relationship between forces and wear," explains the expert from the University of Duisburg-Essen (UDE). Components can show signs of wear in many ways. AI should help to monitor and evaluate wear behavior more precisely in the future.
As reported by Informationsdienst Wissenschaft, Professor Hanke is also working on specific solutions for material problems in the LaufFGL cooperation project (laser deposition welding of functional layers made from shape memory alloys). The aim is to develop a screw connection by the end of 2027 that does not loosen when the temperature changes.
More safety through intelligent metal alloys
Such connections could ensure greater safety in aircraft, for example. They use so-called "intelligent metal alloys" that react independently to temperature changes. These types of alloys are often referred to as shape memory alloys or "smart materials". UDE is developing this application together with partners from science and industry. Hanke is testing how good the mechanical properties of the bolted joint are, considering the friction, wear and corrosion of the weld seams. The project is supported and financed by the European Union and the state of North Rhine-Westphalia.
Prof. Dr. Stefanie Hanke studied mechanical engineering at the UDE from 2002 to 2008. From 2014 to 2017, she worked at the Helmholtz-Zentrum Geestacht. There she investigated solid-phase joining processes from 2014 to 2017 and headed the "Local Modification Processes" group. Before her appointment as a professor, she held the Chair of Materials Engineering at UDE.
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