Gaussian Process for Machine Learning-Based Fatigue Life Prediction Model under Multiaxial Stress–Strain Conditions
In this paper, a new method for fatigue life prediction under multiaxial stress-strain conditions is developed. The method applies machine learning with the Gaussian process for regression to build a fatigue model. The fatigue failure mechanisms are reflected in the model by the application of the p...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/15/21/7797 |
| _version_ | 1797467363167174656 |
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| author | Aleksander Karolczuk Dariusz Skibicki Łukasz Pejkowski |
| author_facet | Aleksander Karolczuk Dariusz Skibicki Łukasz Pejkowski |
| author_sort | Aleksander Karolczuk |
| collection | DOAJ |
| description | In this paper, a new method for fatigue life prediction under multiaxial stress-strain conditions is developed. The method applies machine learning with the Gaussian process for regression to build a fatigue model. The fatigue failure mechanisms are reflected in the model by the application of the physics-based stress and strain invariants as input quantities. The application of the machine learning algorithm solved the problem of assigning an adequate parametric fatigue model to given material and loading conditions. The model was verified using the experimental data on the CuZn37 brass subjected to various cyclic loadings, including non-proportional multiaxial strain paths. The performance of the machine learning-based fatigue life prediction model is higher than the performance of the well-known parametric models. |
| first_indexed | 2024-03-09T18:52:33Z |
| format | Article |
| id | doaj.art-1a691f27936543bfae097692d11e2029 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-09T18:52:33Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-1a691f27936543bfae097692d11e20292023-11-24T05:41:00ZengMDPI AGMaterials1996-19442022-11-011521779710.3390/ma15217797Gaussian Process for Machine Learning-Based Fatigue Life Prediction Model under Multiaxial Stress–Strain ConditionsAleksander Karolczuk0Dariusz Skibicki1Łukasz Pejkowski2Department of Mechanics and Machine Design, Opole University of Technology, Ul. Mikołajczyka 5, 45-271 Opole, PolandFaculty of Mechanical Engineering, UTP University of Science and Technology, Kaliskiego 7, 85-796 Bydgoszcz, PolandFaculty of Mechanical Engineering, UTP University of Science and Technology, Kaliskiego 7, 85-796 Bydgoszcz, PolandIn this paper, a new method for fatigue life prediction under multiaxial stress-strain conditions is developed. The method applies machine learning with the Gaussian process for regression to build a fatigue model. The fatigue failure mechanisms are reflected in the model by the application of the physics-based stress and strain invariants as input quantities. The application of the machine learning algorithm solved the problem of assigning an adequate parametric fatigue model to given material and loading conditions. The model was verified using the experimental data on the CuZn37 brass subjected to various cyclic loadings, including non-proportional multiaxial strain paths. The performance of the machine learning-based fatigue life prediction model is higher than the performance of the well-known parametric models.https://www.mdpi.com/1996-1944/15/21/7797fatigue life predictionCuZn37 brassmachine learning |
| spellingShingle | Aleksander Karolczuk Dariusz Skibicki Łukasz Pejkowski Gaussian Process for Machine Learning-Based Fatigue Life Prediction Model under Multiaxial Stress–Strain Conditions Materials fatigue life prediction CuZn37 brass machine learning |
| title | Gaussian Process for Machine Learning-Based Fatigue Life Prediction Model under Multiaxial Stress–Strain Conditions |
| title_full | Gaussian Process for Machine Learning-Based Fatigue Life Prediction Model under Multiaxial Stress–Strain Conditions |
| title_fullStr | Gaussian Process for Machine Learning-Based Fatigue Life Prediction Model under Multiaxial Stress–Strain Conditions |
| title_full_unstemmed | Gaussian Process for Machine Learning-Based Fatigue Life Prediction Model under Multiaxial Stress–Strain Conditions |
| title_short | Gaussian Process for Machine Learning-Based Fatigue Life Prediction Model under Multiaxial Stress–Strain Conditions |
| title_sort | gaussian process for machine learning based fatigue life prediction model under multiaxial stress strain conditions |
| topic | fatigue life prediction CuZn37 brass machine learning |
| url | https://www.mdpi.com/1996-1944/15/21/7797 |
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