Predicting EHL film thickness parameters by machine learning approaches
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| Định dạng: | Sách |
| Ngôn ngữ: | English |
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Springer
2023
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| Truy cập trực tuyến: | https://link.springer.com/article/10.1007/s40544-022-0641-6 https://dlib.phenikaa-uni.edu.vn/handle/PNK/7953 |
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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 |
8.891695 |