0000-0001-8686-8705
Abstract
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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Article history
- ReceivedUntil the next stage: 34 days
- RevisedUntil the next stage: 27 days
- AcceptedUntil the next stage: 4 days
- Published
About the authors
Mohammad Azadi
Mohammad Azadi was born in Shiraz, Iran in 1983. He received B.Sc. and M.Sc. degrees in mechanical engineering from Shiraz University, Shiraz, Iran and K.N. Toosi University of Technology, Tehran, Iran, respectively, in 2006 and 2008; and then, the Ph.D. degree in mechanical engineering from Sharif University of Technology, Tehran, Iran, in 2013. During his Ph.D., he has awarded an exchange program by the Ministry of Science, Research and Technology and also Irankhodro Powertrain Company, in order to perform a fatigue testing project in University of Leoben, Leoben, Austria, 2012.
From 2008 to 2015, he has worked in Irankhodro Powertrain Company, Tehran, Iran and for last two years, he was a project manager of a national turbo-charged engine. Since 2015, he has been an Assistant Professor in the Faculty of Mechanical Engineering, Semnan University, Semnan, Iran. Now, He is an Associate Professor, since 2019.
He is the author of two chapter-books, two conference proceedings, more than 90 journal articles, about 110 conference papers and 12 patents. He has been also funded to perform 8 research projects by Iranian universities and industries; in addition to one international project, entitled "Iran-Austria Impulse". He is an advisory board of International Journal of Engineering and also a reviewer in different ISI journals, such as International Journal of Fatigue and Materials Science and Engineering A. His research interests include solid mechanics, fatigue, fracture and creep, numerical methods, surface engineering, materials characterization, design of experiments, with the application of engine, aerospace and automotive industries, besides biomechanics. Nowadays, he is working on additive manufacturing to fabricate composites and nano-composites by 3D-printing, in order to evaluate fatigue properties of materials.
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Licence
May be shared and adapted, provided appropriate credit is given. Copyright © 2024 Mohammad Azadi, Mahmood Matin.
Copyright
Authors are allowed to retain both the copyright and the publishing rights of their articles without restrictions.
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