PSI - Issue 81

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ScienceDirect

Procedia Structural Integrity 81 (2026) 116–122

© 2026 The Authors. Copy from the contract: Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of DMDP 2025 organizers In this work, the fatigue life of automotive steel was predicted using the ensemble machine learning methods, namely, random forest and boosted trees. Experimental data describing the dependence of the fatigue crack length a on the number of loading cycles N and the overloading factor R ol for the stress ratio R = 0.1 were used to train and test the models. The random forest method demonstrated high accuracy and stability of prediction with a mean absolute percentage error (MAPE) of 1.12%, while for boosted trees method showed the error equals to 3%. The results confirm the effectiveness of ensemble methods in predicting fatigue crack growth of automotive steels under cyclic loading conditions. 1. Introduction The propagation of fatigue cracks under various loading conditions is an important problem in modern mechanical engineering, as it directly affects the reliability and durability of structures. During operation, automotive structural elements are subjected to cyclic loads that cause the initiation and gradual growth of microcracks. Such cracks act as stress concentrators, in particular, a local increase in stress at the crack tip leads to its progressive growth. As a result, the effective cross-sectional area of the element decreases to such an extent that the part fails suddenly, without significant plastic deformation, even in the case of highly ductile steels. Reliable prediction of fatigue crack growth and determination of residual life are critical for preventing accidents and premature failure. VIII International Conference “In - service Damage of Materials: Diagnostics and Prediction“ (DMDP 2025) Fatigue crack growth prediction of automotive steels using ensemble-based machine learning methods Oleh Yasniy a , Iryna Didych a, *, Dmytro Tymoshchuk a , Iaroslav Pasternak b , Vyacheslav Nykytyuk a , Hryhorii Shymchuk a , Dmytro Radyk a a Ternopil Ivan Puluj National Technical University, Ruska str. 56, Ternopil, 46001, Ukraine b Lesya Ukrainka Volyn National University, 13 Voli Ave., Lutsk, 43025, Ukraine Abstract The propagation of fatigue cracks under various loading conditions is one of the key problems in modern mechanical engineering and the operation of engineering structures. During operation, structural elements of machines and mechanisms are subjected to cyclic loading, which contributes to the initiation and gradual development of cracks. A crack is a source of stress concentration, and a local increase in stress occurs in the area of its tip, leading to its progressive growth. As a result, the cross-section of the part is gradually weakened, and failure occurs suddenly, without noticeable plastic deformation, even in highly ductile metals. Keywords: fatigue crack growth rate; artificial intelligence; machine learning; random forest; boosted trees.

* Corresponding author. Tel.: +380972272074 E-mail address: iryna.didych1101@gmail.com

2452-3216 © 2026 The Authors. Copy from the contract: Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of DMDP 2025 organizers 10.1016/j.prostr.2026.03.021