Item Infomation


Title: 
Predicting EHL film thickness parameters by machine learning approaches
Authors: 
Max, Marian
Jonas, Mursak
Marcel, Bartz
Issue Date: 
2022
Publisher: 
Springer
Abstract: 
Non-dimensional similarity groups and analytically solvable proximity equations can be used to estimate integral fluid film parameters of elastohydrodynamically lubricated (EHL) contacts. In this contribution, we demonstrate that machine learning (ML) and artificial intelligence (AI) approaches (support vector machines, Gaussian process regressions, and artificial neural networks) can predict relevant film parameters more efficiently and with higher accuracy and flexibility compared to sophisticated EHL simulations and analytically solvable proximity equations, respectively. For this purpose, we use data from EHL simulations based upon the full-system finite element (FE) solution and a Latin hypercube sampling.
Description: 
CC BY
URI: 
https://link.springer.com/article/10.1007/s40544-022-0641-6
https://dlib.phenikaa-uni.edu.vn/handle/PNK/7953
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