Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917903115223040 |
|---|---|
| author | Chang, Jiang Basvoju, Deekshith Vakanski, Aleksandar Charit, Indrajit Xian, Min |
| author_facet | Chang, Jiang Basvoju, Deekshith Vakanski, Aleksandar Charit, Indrajit Xian, Min |
| contents | Recent advancements in machine learning-based methods have demonstrated great potential for improved property prediction in material science. However, reliable estimation of the confidence intervals for the predicted values remains a challenge, due to the inherent complexities in material modeling. This study introduces a novel approach for uncertainty quantification in fatigue life prediction of metal materials based on integrating knowledge from physics-based fatigue life models and machine learning models. The proposed approach employs physics-based input features estimated using the Basquin fatigue model to augment the experimentally collected data of fatigue life. Furthermore, a physics-informed loss function that enforces boundary constraints for the estimated fatigue life of considered materials is introduced for the neural network models. Experimental validation on datasets comprising collected data from fatigue life tests for Titanium alloys and Carbon steel alloys demonstrates the effectiveness of the proposed approach. The synergy between physics-based models and data-driven models enhances the consistency in predicted values and improves uncertainty interval estimates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_15057 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Chang, Jiang Basvoju, Deekshith Vakanski, Aleksandar Charit, Indrajit Xian, Min Machine Learning Materials Science I.2.8 Recent advancements in machine learning-based methods have demonstrated great potential for improved property prediction in material science. However, reliable estimation of the confidence intervals for the predicted values remains a challenge, due to the inherent complexities in material modeling. This study introduces a novel approach for uncertainty quantification in fatigue life prediction of metal materials based on integrating knowledge from physics-based fatigue life models and machine learning models. The proposed approach employs physics-based input features estimated using the Basquin fatigue model to augment the experimentally collected data of fatigue life. Furthermore, a physics-informed loss function that enforces boundary constraints for the estimated fatigue life of considered materials is introduced for the neural network models. Experimental validation on datasets comprising collected data from fatigue life tests for Titanium alloys and Carbon steel alloys demonstrates the effectiveness of the proposed approach. The synergy between physics-based models and data-driven models enhances the consistency in predicted values and improves uncertainty interval estimates. |
| title | Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning |
| topic | Machine Learning Materials Science I.2.8 |
| url | https://arxiv.org/abs/2501.15057 |