Machine learning and artificial intelligence in polymer matrix composite materials: A review

Year 2026, Volume: 10 Issue: 1 , 83 - 97 , 20.04.2026

Ayat Bahaa Abdulhussein , İzzet Fatih Şentürk , Mustafa Can Topbasoglu , Ismail Yasin Altayli

https://doi.org/10.35860/iarej.1753185

https://izlik.org/JA88AY77YZ

Abstract

This work reveals the transformative impact of artificial intelligence (AI) and machine learning (ML) techniques in the design, analysis, and performance prediction of composite materials. The review we examined systematically evaluated current AI and ML approaches used in composite material research published between 2005-2024. The main application areas of these approaches are determined as mechanical property prediction, process optimization and structural health monitoring. Analyses have shown that a wide range of models, including Artificial Neural Networks (ANN), Support Vector Machines, Decision Trees and hybrid models, have been successfully applied in composite systems. According to the findings of the study, ML models offer remarkable effectiveness in predicting key mechanical properties such as tensile strength, impact resistance and fatigue behavior, providing accuracy of over 90% in many studies compared to traditional methods. However, limited data access, low interpretability of models and high computational costs are emerging as the main obstacles to the widespread use of these methods on an industrial scale. Future research is expected to focus on developing explainable AI approaches, integrating real-time sensor data into models, and supporting sustainable material design strategies. By integrating data-based methods with materials science, it is concluded that artificial intelligence and machine learning techniques have an important transformative potential in terms of accelerating innovation, reducing costs and improving performance in the development of next-generation composite structures.

Keywords

Artificial intelligence , Composite materials , Design optimization , Machine learning , Property prediction , Structural health monitoring

References

• 1. Aghabalaei Baghaei, K. and S.A. Hadigheh, Artificial neural network prediction of transverse modulus in humid conditions for randomly distributed unidirectional fibre reinforced composites: A micromechanics approach. Composite Structures, 2024. 337: p. 1–16.
• 2. Ahmadi, H., M. Hajikazemi, and W. Van Paepegem, Predicting the elasto-plastic response of short fiber reinforced composites using a computationally efficient multi-scale framework based on physical matrix properties. Composites Part B: Engineering, 2023. 250: p. 110408.
• 3. Alhusban, M., M. Alhusban, and A.A. Alkhawaldeh, The efficiency of using machine learning techniques in fiber-reinforced-polymer applications in structural engineering. Sustainability, 2024. 16(1):p. 11.
• 4. Şen Bayram, A.K., Özgüven, Y., Yazar, N. E., Edgü, E., & Sağlam, S.S., A Proposal for a Material-Focused AI Supported Design Process: From Natural Material to Artificial Intelligence, JCoDe: Journal of Computational Design, (2024). 5(2): p. 211-234. https://doi.org/10.53710/jcode.1512903.
• 5. Chakravarty, S., P. Garg, A. Kumar, M. Agrawal, and P.K. Agnihotri, Deep neural networks based predictive-generative framework for designing composite materials. Modelling and Simulation in Materials Science and Engineering, 2021: p.1-22
• 6. Bhattacharya, S., K. Kalita, R. Čep, and S. Chakraborty, A comparative analysis on prediction performance of regression models during machining of composite materials. Materials, 2021. 14(21): p.6689
• 7. Dev, B., M.A. Rahman, M.J. Islam, M.Z. Rahman, and D. Zhu, Properties prediction of composites based on machine learning models: A focus on statistical index approaches. Materials Today Communications, 2024. 38: p. 107659
• 8. Di Boon, Y., S.C. Joshi, S.K. Bhudolia, and G. Gohel, Recent advances on the design automation for performance-optimized fiber reinforced polymer composite components. Journal of Composites Science, 2020. 4(2): p. 61. https://doi.org/10.3390/jcs4020061
• 9. Ferati, K. and N.G. Adar, Prediction of displacement and stress values of composite materials under load with machine learning models. European Journal of Science and Technology, 2022: p.1-5.
• 10. Gholami, K., F. Ege, and R. Barzegar, Prediction of composite mechanical properties: Integration of deep neural network methods and finite element analysis. Journal of Composites Science, 2023.7(2): p. 54;https://doi.org/10.3390/jcs7020054
• 11. Golkarnarenji, G., M. Naebe, K. Badii, A.S. Milani, R.N. Jazar, and H. Khayyam, A machine learning case study with limited data for prediction of carbon fiber mechanical properties. Computers in Industry, 2019. 105: p. 123–132.
• 12. Goyal, S., Comparison of machine learning techniques for software quality prediction. International Journal of Knowledge and Systems Science, 2020. 11(2): p. 20–40.
• 13. He, M., Y. Wang, K. Ram Ramakrishnan, and Z. Zhang, A comparison of machine learning algorithms for assessment of delamination in fiber-reinforced polymer composite beams. Structural Health Monitoring, 2021. 20(4): p. 1997–2012.
• 14. Hou, W., M. Li, Y. Yang, Z. Liu, and L. Sang, Layup optimization of composite B-pillar under side impact. International Journal of Mechanical Sciences, 2025. 287: p. 109927.
• 15. Huang, S.J., Y. Adityawardhana, and J. Sanjaya, Predicting mechanical properties of magnesium matrix composites with regression models by machine learning. Journal of Composites Science, 2023. 7(9), 347. https://doi.org/10.3390/jcs7090347
• 16. Kibrete, F., T. Trzepieciński, H.S. Gebremedhen, and D.E. Woldemichael, Artificial intelligence in predicting mechanical properties of composite materials. Journal of Composites Science, 2023. 7(9): p.364
• 17. Le, T.-T., Prediction of tensile strength of polymer carbon nanotube composites using practical machine learning method. Journal of Composite Materials, 2020. 55(6): p. 787–811.
• 18. Lee, K., C. Hong, E.H. Lee, and W.H. Yang, Comparison of artificial intelligence methods for prediction of mechanical properties. IOP Conference Series: Materials Science and Engineering, 2020. 967(1): p. 012031.
• 19. Li, J., B. Pradhan, S. Gaur, and J. Thomas, Predictions and strategies learned from machine learning to develop high-performing perovskite solar cells. Advanced Energy Materials, 2019. 9(46): p.1901891.
• 20. Li, Z., H. Qin, Q. Wang, L. Jia, G. Zhang, Y. Li, and Y. Liu, Prediction of allowable compression load for notched composite laminates combining FEA simulation and machine learning. Composite Structures, 2024. 340: p.118188.
• 21. Liu, B., N. Vu-Bac, X. Zhuang, X. Fu, and T. Rabczuk, Stochastic integrated machine learning based multiscale approach for the prediction of thermal conductivity in carbon nanotube reinforced polymeric composites. Composites Science and Technology, 2022. 224 : p.109425.
• 22. Liu, J., L. Wang, Y. He, Y. Zhang, X. Yuan, and W.G. Li, Experimental study of notched tensile strength of large open-hole carbon fiber reinforced polymer laminates at low temperature. Composites Communications, 2023. 39: p.101546.
• 23. Liu, M., H. Li, H. Zhou, H. Zhang, and G. Huang, Development of machine learning methods for mechanical problems associated with fibre composite materials: A review. Composites Communications, 2021. 49: p.101988.
• 24. Liu, X., S. Tian, F. Tao, and W. Yu, A review of artificial neural networks in the constitutive modeling of composite materials. Composites Part B: Engineering, 2024. 224: p.109152.
• 25. Loh, J.Y.Y., K.M. Yeoh, K. Raju, V.N.H. Pham, V.B.C. Tan, and T.E. Tay, A review of machine learning for progressive damage modelling of fiber-reinforced composites. Applied Composite Materials, 2024.31(6): p. 1795-1832.
• 26. Milad, A., S.H. Hussein, A.R. Khekan, M. Rashid, H. Al-Msari, and T.H. Tran, Development of ensemble machine learning approaches for designing fiber-reinforced polymer composite strain prediction model. Engineering with Computers, 2022. 38(4): p. 3625–3637.
• 27. Mohit, H., M.R. Sanjay, S. Siengchin, B. Kanaan, V. Ali, I.M. Alarifi, and T.M.A.A. El-Bagory, Machine learning-based prediction of mechanical and thermal properties of hybrid polymer composites. Polymer Composites, 2024. 45(1): p. 489–506.
• 28. Naya, F., C. González, C.S. Lopes, S. Van der Veen, and F. Pons, Computational micromechanics of the transverse and shear behavior of unidirectional fiber reinforced polymers including environmental effects. Composites Part A, 2017. 92: p. 146–157.
• 29. Palanisamy, S., K. Sureshkumar, C. Santulli, T. Khan, H. Junaedi, and T.A. Sebaey, Machine learning approaches to natural fiber composites: A review of methodologies and applications. BioResources, 2025. 20(1):p. 2321.
• 30. Parsa Mohammadi and Z.J.W., Machine learning for quality prediction in abrasion-resistant material manufacturing process. IEEE Conference Proceedings, 2016: p. 1-4.
• 31. Qiu, C., Y. Han, L. Shanmugam, Y. Zhao, S. Dong, S. Du, and J. Yang, A deep learning-based composite design strategy for efficient selection of material and layup sequences. Composites Science and Technology, 2022. 230: p.109154.
• 32. Qiu, C., Y. Lin, Y. Shen, H. Song, and J. Yang, Designing the geometry of compact tension specimens for easy fracture toughness measurement using reinforcement learning. Journal of Applied Mechanics, 2024. 91(9): p.091001.
• 33. R., B., S.S.D., and G.S.B., Machine learning approaches for prediction of properties of natural fiber composites: Apriori algorithm. Australian Journal of Mechanical Engineering, 2023. 21(5): p. 1790–1805.
• 34. Rahman, A., P. Deshpande, M.S. Radue, G.M. Odegard, S. Gowtham, S. Ghosh, and A.D. Spear, A machine learning framework for predicting the shear strength of carbon nanotube-polymer interfaces. Composites Science and Technology, 2021. 207: p.108627.
• 35. Rayhan, S.B. and M.M. Rahman, Modeling elastic properties of unidirectional composite materials using ANSYS material designer. Procedia Structural Integrity, 2020. 28: p. 1892–1900.
• 36. Robbany, F., B. Pramujati, Suhardjono, M.K. Effendi, B.O.P. Soepangkat, and R. Norcahyo, Multi response prediction of cutting force and delamination in carbon fiber reinforced polymer using backpropagation neural network-genetic algorithm. AIP Conference Proceedings, 2019. 2114(1): p. 030012.
• 37. Seyhan, A.T., G. Tayfur, M. Karakurt, and M. Tanoğlu, Artificial neural network prediction of compressive strength of VARTM processed polymer composites. Computational Materials Science, 2005. 34(1): p. 99–105.
• 38. Shah, V., S. Zadourian, C. Yang, Z. Zhang, and G.X. Gu, Data-driven approach for the prediction of mechanical properties of carbon fiber reinforced composites. Materials Advances, 2022. 3(19): p. 7319–7327.
• 39. Sharma, A., T. Mukhopadhyay, S.M. Rangappa, S. Siengchin, and V. Kushvaha, Advances in computational intelligence of polymer composite materials: Machine learning assisted modeling, analysis and design. Archives of Computational Methods in Engineering, 2022. 29(5): p. 3341–3385.
• 40. Song, L., D. Wang, X. Liu, A. Yin, and Z. Long, Prediction of mechanical properties of composite materials using multimodal fusion learning. Sensors and Actuators A: Physical, 2023. 358: p. 114433.
• 41. Sorour, S.S., C.A. Saleh, and M.A. Shazly, A review on machine learning implementation for predicting and optimizing the mechanical behaviour of laminated fiber-reinforced polymer composites. Heliyon, 2024. 10(13) e33681: p.1-21.
• 42. Tang, J.L., Q.R. Cai, and Y.J. Liu, Prediction of material mechanical properties with support vector machine. in International Conference on Machine Vision and Human-Machine Interface. 2010: p. 592–595.
• 43. Tarawneh, A., A. Alghossoon, E. Saleh, G. Almasabha, Y. Murad, M. Abu-Rayyan, and A. Aldiabat, Machine learning prediction model for shear capacity of FRP-RC slender and deep beams. Sustainability, 2022. 14(23): p.15609
• 44. Wang, C., C. Shen, Q. Cui, C. Zhang, and W. Xu, Tensile property prediction by feature engineering guided machine learning in reduced activation ferritic/martensitic steels. Journal of Nuclear Materials, 2020. 529: p. 151823.
• 45. Xu, Y., H. Weng, X. Ju, H. Ruan, J. Chen, C. Nan, J. Guo, and L. Liang, A method for predicting mechanical properties of composite microstructure with reduced dataset based on transfer learning. Composite Structures, 2021. 275: p. 114444.
• 46. Yang, Y. and G. Liu, Data-driven shear strength prediction of FRP-reinforced concrete beams without stirrups based on machine learning methods. Buildings, 2023. 13(2): p.313
• 47. Yaseen, Z.M., Machine learning models development for shear strength prediction of reinforced concrete beam: A comparative study. Scientific Reports, 2023, 13(1): p.1723
• 48. Yin, B.B. and K.M. Liew, Machine learning and materials informatics approaches for evaluating the interfacial properties of fiber-reinforced composites. Composite Structures, 2021. 273: p.114328
• 49. Yoshimori, S., J. Koyanagi, and R. Matsuzaki, Efficient prediction of fatigue damage analysis of carbon fiber composites using multi-timescale analysis and machine learning. Polymers, 2024. 16(23): p. 3448.
• 50. Yossef, M., M. Noureldin, and A. Alqabbany, Explainable artificial intelligence framework for FRP composites design. Composite Structures, 2024. 341: p.118190.
• 51. Yu, S. and J.S. Colton, Applying hybrid deep neural networks with manually derived layers to model compression strength of angled CFRP in aerospace industry. SSRN Electronic Journal, 2024. Available from: https://ssrn.com/abstract=4743586: p.1-50.