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Development of robust machine learning models for predicting flexural strengths of fiber-reinforced polymeric composites
Hamzat, Abdulhammed K. Salman Umar T. Murad, Md. Shafinur Altay, Okzan Bahçeci, Ersin Asmatulu, Eylem Bakir, Mete Asmatulu, Ramazan
Hamzat, Abdulhammed K.
Salman Umar T.
Murad, Md. Shafinur
Altay, Okzan
Bahçeci, Ersin
Asmatulu, Eylem
Bakir, Mete
Asmatulu, Ramazan
Authors
Hamzat, Abdulhammed K.
Salman Umar T.
Murad, Md. Shafinur
Altay, Okzan
Bahçeci, Ersin
Asmatulu, Eylem
Bakir, Mete
Asmatulu, Ramazan
Salman Umar T.
Murad, Md. Shafinur
Altay, Okzan
Bahçeci, Ersin
Asmatulu, Eylem
Bakir, Mete
Asmatulu, Ramazan
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Original Date
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Issue Date
2025-01-21
Type
Article
Genre
Keywords
Ensembles methods,Fiber-reinforced polymer composites,Flexural strength,Machine learning
Subjects (LCSH)
Citation
Abdulhammed K. Hamzat, Umar T. Salman, Md Shafinur Murad, Ozkan Altay, Ersin Bahceci, Eylem Asmatulu, Mete Bakir, Ramazan Asmatulu, Development of robust machine learning models for predicting flexural strengths of fiber-reinforced polymeric composites, Hybrid Advances, Volume 8, 2025, 100385
Abstract
Fiber-reinforced composites are widely used in engineering applications due to their excellent physical and chemical properties. However, evaluating their flexural properties using conventional experimental techniques is time-consuming, costly, and limited by material and fabrication variations. This study investigates the potential of machine learning (ML) techniques to predict the flexural properties of fiber-reinforced composites accurately and efficiently. Five ML algorithms—Light gradient boosting regressor (LGBR), Extra tree regressor (ETR), Decision tree regressor (DTR), Histogram-based gradient boosting regressor (HGBR), and Adaptive boosting regressor (ABR)—were employed to predict the flexural strengths using both experimental data generated in-house and data collected from open literature. Including heterogeneous data from both sources enhances the robustness and generalizability of the developed models. The results demonstrate that the extra trees regressor (ETR) achieves excellent accuracy when applied to the heterogeneous dataset, with a coefficient of determination (R2) value of 0.94, MAE of 31.97, and RMSE of 47.64, outperforming the other three models. Furthermore, the in-house experimental data yields even higher prediction accuracy, with the best-performing model achieving an impressive R2 value of 0.99, MAE of 9.53, and RMSE of 13.15. The high prediction accuracy achieved, despite the slight variability in data obtained from the literature, highlights the potential use of ML techniques to streamline the development process and reduce the reliance on extensive experimental testing. These robust models take into consideration important composite production parameters to provide design engineers and research scientists with versatile and efficient tools for the prediction of flexural properties of fiber-reinforced composites and related materials for various industries, including aerospace, defense, energy, biomedical and automotive. © 2025 The Authors
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Description
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Publisher
Elsevier B.V.
Journal
Hybrid Advances
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Archival Collection
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DOI
ISSN
2773207X