Shapley additive explanation on machine learning predictions of fatigue lifetimes in piston aluminum alloys under different manufacturing and loading conditions - read online free

Aug 6, 2026

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Shapley additive explanations are used to interpret machine learning predictions of fatigue lifetimes for piston aluminum alloys exposed to distinct manufacturing and loading conditions. The study motivates SHAP based feature attribution to overcome limitations of conventional sensitivity analysis under nonlinear, high-variation behaviors. By linking model outputs to the contribution of individual input features, the approach supports clearer understanding of what governs fatigue life and improves trust in ML-driven lifetime estimation for lightweight piston components.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Introduction

Prior fatigue-life prediction methods

Machine learning for fatigue lifetime estimation

Need for model interpretability using SHAP

M. Matin et alii, Frattura edIntegrità Strutturale, 68 (2024) 357-370; DOI: 10.3221/IGF-ESIS.68.24 | Shapley additive explanation on machine learning predictions of fatigue lifetimes in piston aluminum alloys under different manufacturing and loading conditions
Mahmood Matin, Mohammad Azadi
Faculty of Mechanical Engineering, Semnan University, Semnan, Iran [m_azadi@semnan.ac.ir](m_azadi@semnan.ac.ir), [http://orcid.org/0000-0001-8686-8705](http://orcid.org/0000-0001-8686-8705) | | | --- | --- | | |
Citation: Matin, M., Azadi, M., Shapley additive explanation on machine learning predictions of fatigue lifetimes in piston aluminum alloys under different manufacturing and loading conditions, Frattura ed Integrità Strutturale, 68 (2024) 357-370.
Received: 06.01.2024
Accepted: 07.03.2024
Published: 11.03.2024
Issue: 04.2024
Copyright: © 2024 This is an open access article under the terms of the CC-BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | | KEYWORDS. Machine learning, Bending fatigue, Lifetime estimation, Piston aluminum alloys, Shapley additive explanation. | | | INTRODUCTION
A luminum-silicon alloys have been extensively utilized in internal combustion (IC) engines as a substitute for cast
iron and steel components to decrease the weight, resulting in the reduction of emissions and fuel consumption
[1] . The piston must be robust and durable to withstand thermomechanical fatigue while being lightweight and resistant to wear [2]. Considering the remarkable mechanical properties and lightness, aluminum alloys emerge as one of the best choices for piston manufacturing [3] .
There are several approaches for predicting fatigue lifetimes. Sun and Shang [4] examined the fatigue lifetime estimation of tubular and notched specimens by employing the finite element method (FEM), compared to experimental data under multiaxial loading conditions. Shivachev and Myagkov [5] developed an ANSYS-based method to calculate the transient temperature and strain fields of a piston under different loads, with a focus on evaluating its fatigue lifetime. The fatigue lifetime estimation was executed with a linear regression modeling (LRM) [6] and the neural network technique [7] . Furthermore, Pearson correlation coefficient, permutation feature importance, and accumulated local effects were investigated for the sensitivity analysis of fatigue life modeling inputs [8]. For this objective, SHAP values, known as Shapley | | M. Matin et alii, Frattura edIntegrità Strutturale, 68 (2024) 357-370; DOI: 10.3221/IGF-ESIS.68.24 Additive Explanations, provide a valuable method for determining the significance of each feature on output prediction. Moreover, numerous research studies have been performed using this approach in various fields, such as material engineering [9,10], environmental engineering [11], and mineral engineering [12] . Data science methods were employed to estimate the fatigue characteristics of aluminum alloys. In a study, Abdullatef et al. [13] assessed the accuracy of different machine learning (ML) and artificial intelligence (AI) approaches in predicting the fatigue lifetime of an aluminum alloy based on bending fatigue data. They compared artificial neural networks, support vector machines (SVM) with different kernels, extreme gradient boosting (XGBoost), random forest (RF), and additive neuro-fuzzy inference. They reported that for estimating the fatigue lifetime of 2090-T83 aluminum alloys, the neuro-fuzzy inference method was an accurate model, but XGBoost, due to its simple floating-point nature, was deemed the optimum and fastest one. Yasnii et al. [14] used ML approaches to analyze the fatigue fracture and load ratio effects on D16T aluminum alloys, achieving accurate predictions with the lowest error of 3.2% and 2.5%. Additionally, Lian et al. [15] utilizeda dataset generated for plottin

FAQs

Why are SHAP values used in fatigue lifetime prediction for piston aluminum alloys?

SHAP values provide feature-level explanations, showing the contribution of each input to the predicted fatigue lifetime and enabling interpretation of nonlinear model behavior.

What makes piston aluminum alloys important in this context?

Aluminum alloys offer lightweight and mechanical performance suitable for pistons that must resist thermomechanical fatigue while reducing emissions and fuel consumption.

How do the described machine learning methods relate to fatigue lifetime estimation?

Prior studies compare and apply ML models—such as neural networks, SVM, XGBoost, and random forests—to predict fatigue lifetimes from fatigue and loading-related data.

Aug 6, 2026

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