Machine Learning Based Surrogate Models for the Thermal Behavior of Multi-Plate Clutches

Abstract

Multi-plate clutches play safety-critical roles in many applications. For this reason, correct functioning and safe operation are essential. Spontaneous damages are particularly critical because the failure of the clutch can lead to a failure of the system. Such damage mainly occurs due to very high loads and ultimately very high temperatures. Finite Element Analysis (FEA) enables simulation and prediction of these temperatures, but it is very time-consuming and costly. In order to reduce this computational effort, surrogate models can be created using machine learning (ML) methods, which reproduce the input and output behavior. In this study, various ML methods (polynomial regression, decision tree, support vector regressor, Gaussian process and neural networks) are evaluated with respect to their ability to predict the maximum clutch temperature based on the loads of a slip cycle. The models are examined based on two use cases. In the first use case, the axial force and the speed are varied. In the second use case, the lining thickness is additionally modified. We show that ML approaches fundamentally achieve good results for both use cases. Furthermore, we show that Gaussian process and backpropagation neural network provide the best results in both cases and that the requirement to generate predictions during operation is fulfilled.