Machine learning based surrogate modelling for the prediction of maximum contact temperature in EHL line contacts

The present study aims at predicting the maximum temperature in line contacts depending on operating conditions. For this purpose, a thermo-elastohydrodynamic lubrication (TEHL) simulation model of a line contact is used to calculate the maximum temperature for a wide range of parameters. Subsequent...

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Bibliographic Details
Published in:Tribology international 2023-01, Vol.179, p.108166, Article 108166
Main Authors: Singh, A., Wolf, M., Jacobs, G., König, F.
Format: Article
Language:English
Subjects:
Feature engineering
Flash temperature
Machine learning
Rolling contact
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Summary:The present study aims at predicting the maximum temperature in line contacts depending on operating conditions. For this purpose, a thermo-elastohydrodynamic lubrication (TEHL) simulation model of a line contact is used to calculate the maximum temperature for a wide range of parameters. Subsequently, a neural networks (NN) approach is used to develop a surrogate model that is able to predict the maximum temperature on the basis of the operational parameters. The influence of different NN architectures and transfer functions on the accuracy is shown. A good agreement with a correlation coefficient (R) greater than 0.997 is achieved for a NN with two hidden layers. Furthermore, the impact of feature engineering on the prediction accuracy with limited data sets is presented. •Local temperature detection in rolling contact bearings on the basis of TEHL simulations.•Efficient temperature prediction with a neural network-based surrogate model.•Influence of feature engineering on the performance of neural network model.•Impact of neural network architectures on the prediction accuracy.
ISSN:0301-679X
1879-2464
DOI:10.1016/j.triboint.2022.108166