Fraunhofer Institute for Microstructure of Materials and Systems IMWS
Microplastic particles generated by tire abrasion are a significant burden on our environment. Improved solutions for abrasion resistance can help reduce these emissions. In the "KI-RAM" project, Fraunhofer IMWS, Rösler Tyre Innovators GmbH & Co. KG, DENKweit GmbH, iMes Solutions GmbH, and the University of Paderborn have developed solutions for this. They combine a novel abrasion sensor in the tire with AI methods for data evaluation. This can, for example, support freight forwarders in selecting the right tires and timing service intervals. The partners presented their results today at a final event of the project.
Every year, tire abrasion alone generates around 100,000 tons of microplastics in Germany. Reducing this tire abrasion – just like avoiding CO₂ emissions – is a sustainability aspect that has become increasingly important in tire optimization in recent years. In addition to environmental policy requirements, this is also due to the increased demands on the abrasion resistance of tires for electric vehicles.
Precise knowledge of factors influencing abrasion
Tire abrasion depends on a variety of factors. In addition to the properties of the tire (rubber compound of the tire tread, tire load due to vehicle weight, tire pressure), other aspects (driving behavior, road surface, weather) also play a crucial role. Since the influence of all these factors is not sufficiently known in detail, complex road tests under defined conditions remain the only established method for comparatively evaluating the abrasion properties of tires. However, the results of such tests only reflect the abrasion behavior of tires in real-world use to a limited extent.
"The key to reducing tire abrasion is to optimize tire selection and service life under specific conditions. This requires accurate knowledge of the factors that influence abrasion so that they can be considered during tire production. We have gained important insights into all these aspects in the "KI-RAM" project says Prof. Dr. Mario Beiner, Group Manager of "Microstructure-based Material Design" at Fraunhofer IMWS.
Retrofittable sensor determines tread depth of truck tires
The methods developed in "KI-RAM" make it possible to better predict tire wear, better understand relevant influencing factors, and reduce traffic-related microparticle emissions in a targeted manner. As a key result, the project partners have developed a retrofittable abrasion sensor for inline measurements of the tread depth of truck tires. This is combined with an AI-based software solution that predicts the remaining service life and, in combination with additional data from the vehicle and the environment, enables an assessment of the significance of various factors influencing tire wear.
The necessary data were collected during field tests with municipal vehicles and combined with data taken from relevant databases on weather and road conditions. The software solution developed can, for example, support freight forwarders in selecting tires and allows also optimizing service intervals. Since the AI software is trained individually by each freight forwarder when using inline tire abrasion sensors, the results mirror precisely their own conditions, and no data have to be shared with competitors. "These retrofittable abrasion sensors for truck tires, in combination with innovative AI-based software solutions, can make a significant contribution to reducing traffic-related microparticle emissions," says Paul Rösler, managing director of Rösler Tyre Innovators GmbH & Co. KG and network coordinator for the "KI-RAM" project, which was funded by the German Federal Ministry of Transport.
Fraunhofer IMWS contributed its broad expertise in materials science relating to rubber compounds for tire treads and its experience in developing AI-based methods for material and component optimization to the project. Various laboratory indicators for abrasion were determined for selected tread compounds and compared with the results of road tests in which retreaded tires with identical tread compounds were used. This comparison was used to verify specific correlations between field tests and laboratory indicators. In addition, the development of novel evaluation methods based on AI analysis of tire images taken with conventional and infrared cameras during the field tests was also supported. "This provides interesting insights that will be incorporated into further activities to improve the abrasion properties of rubber compounds for tire treads and to reduce traffic-related microparticle emissions," says Mario Beiner.
(February 12, 2026)