Shapley additive explanation on machine learning predictions of fatigue lifetimes in piston aluminum alloys under different manufacturing and loading conditions
Various input variables, including corrosion time, fretting force, stress, lubrication, heat-treating, and nano-particles, were evaluated by modeling of stress-controlled fatigue lifetimes in AlSi12CuNiMg aluminum alloy of the engine pistons with different machine learning (ML) techniques. Bending...
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| Format: | Article |
| Language: | English |
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Gruppo Italiano Frattura
2024-03-01
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| Series: | Frattura ed Integrità Strutturale |
| Subjects: | |
| Online Access: | https://fracturae.com/index.php/fis/article/view/4790 |
| _version_ | 1797199893296578560 |
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| author | Mohammad Azadi Mahmood Matin |
| author_facet | Mohammad Azadi Mahmood Matin |
| author_sort | Mohammad Azadi |
| collection | DOAJ |
| description |
Various input variables, including corrosion time, fretting force, stress, lubrication, heat-treating, and nano-particles, were evaluated by modeling of stress-controlled fatigue lifetimes in AlSi12CuNiMg aluminum alloy of the engine pistons with different machine learning (ML) techniques. Bending fatigue experiments were conducted through cyclic loading with zero mean stress, and then experimental data was predicted by five different ML-based models. Moreover, when the optimal ML prediction model was found, it was analyzed using the Shapley additive explanation (SHAP) values method. Results illustrated that extreme gradient boosting (XGBoost) had superior data for estimations, with average training coefficients of determination of at least 63% and 90%, respectively for fatigue lifetime and its logarithmic value. The SHAP values interpretation of the XGBoost model revealed that fretting force, stress, and corrosion time were the most significant inputs in estimating the logarithm values of fatigue lifetimes, respectively.
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| first_indexed | 2024-04-24T07:22:59Z |
| format | Article |
| id | doaj.art-45557974f15c4ec8b7450dd00ed2e88d |
| institution | Directory Open Access Journal |
| issn | 1971-8993 |
| language | English |
| last_indexed | 2024-04-24T07:22:59Z |
| publishDate | 2024-03-01 |
| publisher | Gruppo Italiano Frattura |
| record_format | Article |
| series | Frattura ed Integrità Strutturale |
| spelling | doaj.art-45557974f15c4ec8b7450dd00ed2e88d2024-04-21T00:21:03ZengGruppo Italiano FratturaFrattura ed Integrità Strutturale1971-89932024-03-01186810.3221/IGF-ESIS.68.24Shapley additive explanation on machine learning predictions of fatigue lifetimes in piston aluminum alloys under different manufacturing and loading conditionsMohammad Azadi0https://orcid.org/0000-0001-8686-8705Mahmood Matin1https://orcid.org/0009-0002-0412-8581Faculty of Mechanical Engineering, Semnan University, Semnan, IranFaculty of Mechanical Engineering, Semnan University, Semnan, Iran Various input variables, including corrosion time, fretting force, stress, lubrication, heat-treating, and nano-particles, were evaluated by modeling of stress-controlled fatigue lifetimes in AlSi12CuNiMg aluminum alloy of the engine pistons with different machine learning (ML) techniques. Bending fatigue experiments were conducted through cyclic loading with zero mean stress, and then experimental data was predicted by five different ML-based models. Moreover, when the optimal ML prediction model was found, it was analyzed using the Shapley additive explanation (SHAP) values method. Results illustrated that extreme gradient boosting (XGBoost) had superior data for estimations, with average training coefficients of determination of at least 63% and 90%, respectively for fatigue lifetime and its logarithmic value. The SHAP values interpretation of the XGBoost model revealed that fretting force, stress, and corrosion time were the most significant inputs in estimating the logarithm values of fatigue lifetimes, respectively. https://fracturae.com/index.php/fis/article/view/4790machine learningBending fatigueLifetime estimationEngine pistonAluminum-silicon alloy |
| spellingShingle | Mohammad Azadi Mahmood Matin Shapley additive explanation on machine learning predictions of fatigue lifetimes in piston aluminum alloys under different manufacturing and loading conditions Frattura ed Integrità Strutturale machine learning Bending fatigue Lifetime estimation Engine piston Aluminum-silicon alloy |
| title | Shapley additive explanation on machine learning predictions of fatigue lifetimes in piston aluminum alloys under different manufacturing and loading conditions |
| title_full | Shapley additive explanation on machine learning predictions of fatigue lifetimes in piston aluminum alloys under different manufacturing and loading conditions |
| title_fullStr | Shapley additive explanation on machine learning predictions of fatigue lifetimes in piston aluminum alloys under different manufacturing and loading conditions |
| title_full_unstemmed | Shapley additive explanation on machine learning predictions of fatigue lifetimes in piston aluminum alloys under different manufacturing and loading conditions |
| title_short | Shapley additive explanation on machine learning predictions of fatigue lifetimes in piston aluminum alloys under different manufacturing and loading conditions |
| title_sort | shapley additive explanation on machine learning predictions of fatigue lifetimes in piston aluminum alloys under different manufacturing and loading conditions |
| topic | machine learning Bending fatigue Lifetime estimation Engine piston Aluminum-silicon alloy |
| url | https://fracturae.com/index.php/fis/article/view/4790 |
| work_keys_str_mv | AT mohammadazadi shapleyadditiveexplanationonmachinelearningpredictionsoffatiguelifetimesinpistonaluminumalloysunderdifferentmanufacturingandloadingconditions AT mahmoodmatin shapleyadditiveexplanationonmachinelearningpredictionsoffatiguelifetimesinpistonaluminumalloysunderdifferentmanufacturingandloadingconditions |