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Predicting EHL film thickness parameters by machine learning approaches

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Main Authors: Max, Marian, Jonas, Mursak, Marcel, Bartz
Format: Book
Language:English
Published: Springer 2023
Subjects:
elastohydrodynamically lubricated
finite element
Online Access:https://link.springer.com/article/10.1007/s40544-022-0641-6
https://dlib.phenikaa-uni.edu.vn/handle/PNK/7953
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spelling oai:localhost:PNK-79532023-04-14T08:09:14Z Predicting EHL film thickness parameters by machine learning approaches Max, Marian Jonas, Mursak Marcel, Bartz elastohydrodynamically lubricated finite element CC BY 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. 2023-04-14T08:09:14Z 2023-04-14T08:09:14Z 2022 Book https://link.springer.com/article/10.1007/s40544-022-0641-6 https://dlib.phenikaa-uni.edu.vn/handle/PNK/7953 en application/pdf Springer
institution Digital Phenikaa
collection Digital Phenikaa
language English
topic elastohydrodynamically lubricated
finite element
spellingShingle elastohydrodynamically lubricated
finite element
Max, Marian
Jonas, Mursak
Marcel, Bartz
Predicting EHL film thickness parameters by machine learning approaches
description CC BY
format Book
author Max, Marian
Jonas, Mursak
Marcel, Bartz
author_facet Max, Marian
Jonas, Mursak
Marcel, Bartz
author_sort Max, Marian
title Predicting EHL film thickness parameters by machine learning approaches
title_short Predicting EHL film thickness parameters by machine learning approaches
title_full Predicting EHL film thickness parameters by machine learning approaches
title_fullStr Predicting EHL film thickness parameters by machine learning approaches
title_full_unstemmed Predicting EHL film thickness parameters by machine learning approaches
title_sort predicting ehl film thickness parameters by machine learning approaches
publisher Springer
publishDate 2023
url https://link.springer.com/article/10.1007/s40544-022-0641-6
https://dlib.phenikaa-uni.edu.vn/handle/PNK/7953
_version_ 1763180887978016768
score 8.881002

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