Einfluss von Vorschädigung auf das Reibungsverhalten nasslaufender Lamellenkupplungen im Dauerschaltbetrieb

This research paper presents a comprehensive methodology for analyzing wet clutches, focusing on their intricate thermomechanical behavior. The study combines advanced encoding techniques, such as Principal Component Analysis (PCA), with metamodeling, to efficiently predict pressure and temperature distributions on friction surfaces. By parametrically varying input parameters and utilizing Finite Element Method (FEM) simulations, we generate a dataset comprising 200 simulations, divided into training and testing sets. Our findings indicate that PCA encoding effectively reduces data dimensionality while preserving essential information. Notably, the study reveals that only a few PCA components are required for accurate encoding: two components for temperature distribution and pressure, and three components for heat flux density. We compare various metamodeling techniques, including Linear Regression, Decision Trees, Random Forest, Support Vector Regression, Gaussian Processes, and Neural Networks. The results underscore the varying performance of these techniques, with Random Forest excelling in mechanical metamodeling and Neural Networks demonstrating superiority in thermal metamodeling.

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

Schneider

Bedrikow

Dietsch

et al. 2022

ASI

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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.

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Einfluss von Vorschädigung auf das Reibungsverhalten nasslaufender Lamellenkupplungen im Dauerschaltbetrieb

Cited by 5 publications

References 29 publications

Study on influence of oil discharging hole on clutch oil charging and discharging characteristics

Study on influence of oil discharging hole on clutch oil charging and discharging characteristics

Enhanced prediction of thermomechanical systems using machine learning, PCA, and finite element simulation

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

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