Vater, KerstinKerstinVaterStender, MertenMertenStenderHoffmann, NorbertNorbertHoffmann2022-08-302022-08-302022-08-1892nd Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM 2022)http://hdl.handle.net/11420/13470Friction contacts can be found in almost all mechanical systems and are often of great technical importance. However, they are usually difficult to describe, and their behavior and influence on the whole system are hard to predict accurately. Modern product design and system operation strongly benefit from numerical simulation approaches today, but reliable friction models still represent a major challenge in this context. To tackle this problem, we employ neural network regression to capture the characteristics of frictional contacts and make them accessible for numerical methods in a minimal intrusive fashion. In particular, we test our approach using a Finite Element model of a 2D cantilever beam subject to stick-slip vibrations induced by a moving conveyor belt at its free end. As a reference solution, we perform a transient analysis based on a simple analytical friction model, where the kinetic friction force only depends on the normal load and the relative sliding velocity. We take the same friction model, add some artificial noise to mimic uncertainties coming with experimental measurements, and pick a limited set of data points to train a regression neural network. The machine learning friction model is then deployed in the Finite Element code to predict the kinetic friction force acting on the beam tip during the slip phases. The deflection curves obtained by the transient numerical analysis using the new neural network friction model agree well with the reference solution based on the underlying analytical model. The results indicate that data-driven approaches may also be capable of capturing more complex frictional contacts, including effects of temperature, humidity, and load history. The trained neural network friction models can then be employed in numerical simulations in a minimally intrusive manner. This approach opens up new possibilities to predict individual mechanical system behavior as accurately as possible.enhttps://creativecommons.org/licenses/by/4.0/Structural dynamicsFrictionMachine learningNonlinear dynamicsHybrid modelingPhysikTechnikIngenieurwissenschaftenConference Presentation10.15480/882.456110.15480/882.456110.15480/882.5045Presentation