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Machine learning for fatigue lifetime predictions in 3D-printed polylactic acid biomaterials based on interpretable extreme gradient boosting model

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Machine learning for fatigue lifetime predictions in 3D-printed polylactic acid biomaterials based on interpretable extreme gradient boosting model. / Nasiri, Hamid; Dadashi, Ali; Azadi, Mohammad.
In: MATERIALS TODAY COMMUNICATIONS, Vol. 39, 109054, 30.06.2024.

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Nasiri H, Dadashi A, Azadi M. Machine learning for fatigue lifetime predictions in 3D-printed polylactic acid biomaterials based on interpretable extreme gradient boosting model. MATERIALS TODAY COMMUNICATIONS. 2024 Jun 30;39:109054. Epub 2024 May 6. doi: 10.1016/j.mtcomm.2024.109054

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@article{d597c875e43f444d976cb69d5dcca6ae,
title = "Machine learning for fatigue lifetime predictions in 3D-printed polylactic acid biomaterials based on interpretable extreme gradient boosting model",
abstract = "Modeling of the fatigue lifetimes in 3D-printed biomaterials of Polylactic acid (PLA) is presented in this article based on machine learning (ML) techniques of interpretable extreme gradient boosting (XGBoost) and Shapley additive explanations. For this objective, standard testing samples were additive-manufactured from PLA under different 3D printing parameters. Then, the fatigue experiments were performed on specimens under various stress levels. Based on these data, three ML methods were utilized for modeling the PLA fatigue lifetimes, including XGBoost, random forest, and support vector regression, besides a common nonlinear regression analysis. The obtained results indicated that XGBoost had superior modeling results, compared to other ML techniques and the regression analysis. The coefficient of determination was 97.66 % with a scatter-band of ±1.3, which was a narrow scatter in fatigue modeling.",
keywords = "3D printing, Biomaterials, Extreme gradient boosting, Fatigue lifetime, Machine learning",
author = "Hamid Nasiri and Ali Dadashi and Mohammad Azadi",
year = "2024",
month = jun,
day = "30",
doi = "10.1016/j.mtcomm.2024.109054",
language = "English",
volume = "39",
journal = "MATERIALS TODAY COMMUNICATIONS",
issn = "2352-4928",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Machine learning for fatigue lifetime predictions in 3D-printed polylactic acid biomaterials based on interpretable extreme gradient boosting model

AU - Nasiri, Hamid

AU - Dadashi, Ali

AU - Azadi, Mohammad

PY - 2024/6/30

Y1 - 2024/6/30

N2 - Modeling of the fatigue lifetimes in 3D-printed biomaterials of Polylactic acid (PLA) is presented in this article based on machine learning (ML) techniques of interpretable extreme gradient boosting (XGBoost) and Shapley additive explanations. For this objective, standard testing samples were additive-manufactured from PLA under different 3D printing parameters. Then, the fatigue experiments were performed on specimens under various stress levels. Based on these data, three ML methods were utilized for modeling the PLA fatigue lifetimes, including XGBoost, random forest, and support vector regression, besides a common nonlinear regression analysis. The obtained results indicated that XGBoost had superior modeling results, compared to other ML techniques and the regression analysis. The coefficient of determination was 97.66 % with a scatter-band of ±1.3, which was a narrow scatter in fatigue modeling.

AB - Modeling of the fatigue lifetimes in 3D-printed biomaterials of Polylactic acid (PLA) is presented in this article based on machine learning (ML) techniques of interpretable extreme gradient boosting (XGBoost) and Shapley additive explanations. For this objective, standard testing samples were additive-manufactured from PLA under different 3D printing parameters. Then, the fatigue experiments were performed on specimens under various stress levels. Based on these data, three ML methods were utilized for modeling the PLA fatigue lifetimes, including XGBoost, random forest, and support vector regression, besides a common nonlinear regression analysis. The obtained results indicated that XGBoost had superior modeling results, compared to other ML techniques and the regression analysis. The coefficient of determination was 97.66 % with a scatter-band of ±1.3, which was a narrow scatter in fatigue modeling.

KW - 3D printing

KW - Biomaterials

KW - Extreme gradient boosting

KW - Fatigue lifetime

KW - Machine learning

U2 - 10.1016/j.mtcomm.2024.109054

DO - 10.1016/j.mtcomm.2024.109054

M3 - Journal article

AN - SCOPUS:85192179686

VL - 39

JO - MATERIALS TODAY COMMUNICATIONS

JF - MATERIALS TODAY COMMUNICATIONS

SN - 2352-4928

M1 - 109054

ER -