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 f...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Gruppo Italiano Frattura
2024-04-01
|
| Series: | Fracture and Structural Integrity |
| Subjects: | |
| Online Access: | https://www.fracturae.com/index.php/fis/article/view/4790/4011 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841562235925692416 |
|---|---|
| author | Mahmood Matin Mohammad Azadi |
| author_facet | Mahmood Matin Mohammad Azadi |
| author_sort | Mahmood Matin |
| 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 |
| format | Article |
| id | doaj-art-45557974f15c4ec8b7450dd00ed2e88d |
| institution | Kabale University |
| issn | 1971-8993 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Gruppo Italiano Frattura |
| record_format | Article |
| series | Fracture and Structural Integrity |
| spelling | doaj-art-45557974f15c4ec8b7450dd00ed2e88d2025-01-03T01:03:58ZengGruppo Italiano FratturaFracture and Structural Integrity1971-89932024-04-01186835737010.3221/IGF-ESIS.68.2410.3221/IGF-ESIS.68.24Shapley additive explanation on machine learning predictions of fatigue lifetimes in piston aluminum alloys under different manufacturing and loading conditionsMahmood MatinMohammad AzadiVarious 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, respectivelyhttps://www.fracturae.com/index.php/fis/article/view/4790/4011machine learningbending fatiguelifetime estimationpiston aluminum alloysshapley additive explanation |
| spellingShingle | Mahmood Matin Mohammad Azadi Shapley additive explanation on machine learning predictions of fatigue lifetimes in piston aluminum alloys under different manufacturing and loading conditions Fracture and Structural Integrity machine learning bending fatigue lifetime estimation piston aluminum alloys shapley additive explanation |
| 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 piston aluminum alloys shapley additive explanation |
| url | https://www.fracturae.com/index.php/fis/article/view/4790/4011 |
| work_keys_str_mv | AT mahmoodmatin shapleyadditiveexplanationonmachinelearningpredictionsoffatiguelifetimesinpistonaluminumalloysunderdifferentmanufacturingandloadingconditions AT mohammadazadi shapleyadditiveexplanationonmachinelearningpredictionsoffatiguelifetimesinpistonaluminumalloysunderdifferentmanufacturingandloadingconditions |