Prediction of Mechanical Properties for Advanced Engineering Applications utilizing Polymer Composite Materials by Machine Learning

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 14 | 03 | Page :
By

R. Indhu,

S. Palpandi,

W.V. Sherlin Sherly,

M. Indirani,

M.D. Boomija,

S. Suruthi,

R. Balasubramaniyan,

  1. Assistant Professor, Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Jeppiaar Nagar, SH 49A, Chennai, Tamil Nadu, India
  2. Assistant Professor (Senior Grade), Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India
  3. Assistant Professor, Department of Artificial Intelligence and Data Science, St. Joseph’s Engineering College, OMR road, Kamaraj Nagar, Semmancheri, Tamil Nadu, India
  4. Associate Professor, Department of Computer Science and Business Systems, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, Tamil Nadu, India
  5. Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, SIMATS, Chennai, Tamil Nadu, India
  6. Assistant Professor, Department of Electronics and Communication Engineering, Easwari Engineering College, Chennai, Tamil Nadu, India
  7. Assistant Professor, Department of Artificial Intelligence and Data Science, Jeppiaar Institute of Technology, Kancheepuram, Tamil Nadu, India

Abstract

Polymer composites show great promise as engineering materials because of their mechanical performance, resistance to corrosion, lightweight nature, and adaptability in design. Aerospace, automotive, biomedical, maritime, and civil engineers all rely on mechanical property prediction to cut down on trial expenses, expedite product development, and optimize material selection. Speedy design optimization is not possible using traditional numerical and experimental methods due to the high costs associated with material characterisation, computational power, and time. The mechanical properties of advanced polymer composite materials can be predicted utilizing data on their composition, reinforcing qualities, manufacturing conditions, and environmental factors through the use of a machine learning framework developed in this study. A.I., Gradient Boosting, Random Forest, and Support Vector Regression Raising the predicted values of toughness, elastic modulus, flexural strength, impact, and compression. Model generalizability and prediction accuracy are enhanced through data preprocessing, feature engineering, training, hyperparameter adjustment, and cross-validation. In different operating settings, ensemble-based and neural network models outperform regression in predicting composite behavior. Statistical measures like MAE, RMSE, and R³ are used to evaluate performance. Intelligent material design is made possible through the identification of mechanical performance characteristics to avoid expensive laboratory testing. Engineers can use machine learning to speed up the screening process for composite formulations and choose the best material combinations. Enhancing decision-making, decreasing development cycles, and promoting new polymer composites in next-generation engineering systems are all benefits of a data-driven materials engineering prediction framework that is efficient, scalable, and accurate.

Keywords: Mechanical Property Prediction, Random Forest, Artificial Neural Networks, Feature Engineering, Materials Informatics, Predictive Modeling

How to cite this article: R. Indhu, S. Palpandi, W.V. Sherlin Sherly, M. Indirani, M.D. Boomija, S. Suruthi, R. Balasubramaniyan. Prediction of Mechanical Properties for Advanced Engineering Applications utilizing Polymer Composite Materials by Machine Learning. Journal of Polymer & Composites. 2026; 14(03):-.
How to cite this URL: R. Indhu, S. Palpandi, W.V. Sherlin Sherly, M. Indirani, M.D. Boomija, S. Suruthi, R. Balasubramaniyan. Prediction of Mechanical Properties for Advanced Engineering Applications utilizing Polymer Composite Materials by Machine Learning. Journal of Polymer & Composites. 2026; 14(03):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=250587

References

  1. Bukvić, M., Milojević, S., Gajević, S., Đorđević, M., & Stojanović, B. (2025). Production technologies and application of polymer composites in engineering: A review. Polymers, 17(16), 2187.
  2. Khan, M. K. A., Mujahid, Y., Alahmed, N., et al. (2026). A review of multifunctional hybrid fiber-reinforced polymer (HFRP) composites for aerospace applications. Advanced Composites and Hybrid Materials, 9, 118.
  3. Kamarudin, S. H., Mohd Basri, M. S., Rayung, M., Abu, F., Ahmad, S., Norizan, M. N., et al. (2022). A review on natural fiber reinforced polymer composites (NFRPC) for sustainable industrial applications. Polymers, 14(17), 3698.
  4. Shifa, S. S., & Zhang, N. (2026). Progress and perspectives on plant-based natural fiber-reinforced polymer composites: Structure–property relationships and emerging design strategies. Journal of Polymer Research, 33, 254.
  5. Rahman, M. M., Ismail, A. E., Ramli, M. F., Rashid, A. H. A., Khan, T., Ahmed, O. S., & Sebaey, T. A. (2026). Review of predictions of tensile and flexural properties of fiber-reinforced composites using machine learning models. Journal of Composites Science, 10(4), 212.
  6. Dornheim, J., Morand, L., Nallani, H. J., et al. (2024). Neural networks for constitutive modeling: From universal function approximators to advanced models and the integration of physics. Archives of Computational Methods in Engineering, 31, 1097–1127.
  7. Wang, Z., & Cudmani, R. (2026). Practical sparse data-driven constitutive modeling via transfer learning in physics-encoded neural networks. Scientific Reports, 16(1), 636.
  8. Kibrete, F., Trzepieciński, T., Gebremedhen, H. S., & Woldemichael, D. E. (2023). Artificial intelligence in predicting mechanical properties of composite materials. Journal of Composites Science, 7(9), 364.
  9. Nikooharf, M. H., Shirinbayan, M., Arabkoohi, M., et al. (2024). Machine learning in polymer additive manufacturing: A review. International Journal of Material Forming, 17, 52.
  10. Bargam, B., Boudhar, A., Kinnard, C., et al. (2024). Evaluation of the support vector regression (SVR) and the random forest (RF) models accuracy for streamflow prediction under a data-scarce basin in Morocco. Discover Applied Sciences, 6, 306.
  11. Dou, B., Zhu, Z., Merkurjev, E., Ke, L., Chen, L., Jiang, J., et al. (2023). Machine learning methods for small data challenges in molecular science. Chemical Reviews, 123(13), 8736–8780.
  12. Huang, G., Guo, Y., Chen, Y., & Nie, Z. (2023). Application of machine learning in material synthesis and property prediction. Materials, 16(17), 5977.
  13. Schmidt, J., Marques, M. R. G., Botti, S., et al. (2019). Recent advances and applications of machine learning in solid-state materials science. npj Computational Materials, 5, 83.
  14. Naser, M. Z. (2026). A review of machine learning with small and limited data. Journal of Big Data, 13, 18.
  15. Bukvić, M., Milojević, S., Gajević, S., Đorđević, M., & Stojanović, B. (2025). Production technologies and application of polymer composites in engineering: A review. Polymers, 17(16), 2187.
  16. Uddin, M. H., Mulla, M. H., Abedin, T., et al. (2025). Advances in natural fiber polymer and PLA composites through artificial intelligence and machine learning integration. Journal of Polymer Research, 32, 76.
  17. Rahman, M. M., Islam, S., Mubasshira, Islam, M. S., Ahammad, R., Islam, M. A., et al. (2026). Polymer composites in additive manufacturing: Current technologies, applications, and emerging trends. Polymers, 18(2), 192.
  18. Murad, M. S., Hamzat, A. K., Unlu, N. A., et al. (2026). Comparative analysis of machine learning models for predicting the mechanical properties of aerospace grade fiber composites. Machine Learning: Computational Science and Engineering, 2, 20.
  19. Rahman, M. M., Ismail, A. E., Ramli, M. F., Rashid, A. H. A., Khan, T., Ahmed, O. S., & Sebaey, T. A. (2026). Review of predictions of tensile and flexural properties of fiber-reinforced composites using machine learning models. Journal of Composites Science, 10(4), 212.
  20. Gomaa, A. M., Ahmed, M. S., Lotfy, E. M., et al. (2026). Machine learning-driven prediction of mechanical properties of lightweight concrete based on experimental data. Scientific Reports, 16, 17187.
  21. Islam, S., Abedin, M. Z., & Ashraf, M. A. (2025). Predicting split-tensile strength of fiber reinforced concrete with lathe-waste steel scrap using artificial neural network. Discover Concrete and Cement, 1, 13.
  22. Turco, C., Funari, M. F., Teixeira, E., & Mateus, R. (2021). Artificial neural networks to predict the mechanical properties of natural fibre-reinforced compressed earth blocks (CEBs). Fibers, 9(12), 78.
  23. Wang, Y., Sun, J., Liu, X., et al. (2026). Ensemble learning-based strength prediction and model interpretability analysis of engineered cementitious composites. AI in Civil Engineering, 5, 7.
  24. Srinath, E., Venkateswara Reddy, K., Manohar, G., et al. (2026). Data-driven prediction of microhardness and tensile strength in microwave-sintered ZrC reinforced AA7075/SiC hybrid composites using machine learning. Scientific Reports, 16, 15971.
  25. Davis Hans, S. J., Muthukumaran, M., Kumaresan, K., et al. (2025). Hybrid experimental and machine learning approach for optimizing abrasive wear of microcrystalline cellulose modified hemp/bamboo fiber composites. Scientific Reports, 15, 42216.
  26. Tiwari, S., & Gupta, A. (2026). A machine learning framework for the reconstruction of composite fatigue and fracture properties: A synthetic data study. Materials, 19(6), 1131.
  27. Gowda, H. D. S., Raju, H., Kumar, V. G. P., et al. (2026). A review of recent machine learning advances for predicting and optimizing mechanical and tribological properties of fiber-reinforced thermoset polymer nanocomposites. Discover Applied Sciences, 8, 636.
  28. Kokash, H., Kokash, M., Bany-Ata, A., Baqain, S., & Shakoor, M. (2026). Recent advances in artificial intelligence and machine learning for life cycle-wide additive manufacturing: A comprehensive review. Machines, 14(5), 550.
  29. Ratchagar, V., et al. (2024). Investigation of glucose biosensors by non-enzymatic photoluminescent detection using oleic acid treated Ag₂S nanoparticles. Materials Chemistry and Physics, 316, 129116.
  30. Koukaras, P., & Tjortjis, C. (2025). Data preprocessing and feature engineering for data mining: Techniques, tools, and best practices. AI, 6(10), 257.
  31. Hiremath, P., Bhat, S. K., K., J. P., Rao, P. K., Ambiger, K. D., B. R. N., M., et al. (2025). Data-driven prediction of polymer nanocomposite tensile strength through Gaussian process regression and Monte Carlo simulation with enhanced model reliability. Journal of Composites Science, 9(7), 364.
  32. Malashin, I. P., Martysyuk, D., Nelyub, V., et al. (2025). Deep learning for property prediction of natural fiber polymer composites. Scientific Reports, 15, 27837.
  33. Bisoyi, S., Salins, S. S., & Sachidananda, H. K. (2025). Enhancing mechanical properties of carbon fiber-reinforced composites via acid activation. Discover Applied Sciences, 7, 443.
  34. Jamshaid, H., Mishra, R. K., Chandan, V., Nazari, S., Shoaib, M., Bizet, L., Jirku, P., Muller, M., et al. (2023). Hybrid thermoplastic composites from basalt- and Kevlar-woven fabrics: Comparative analysis of mechanical and thermomechanical performance. Polymers, 15(7), 1744.
  35. Nurazzi, N. M., Asyraf, M. R. M., Fatimah Athiyah, S., Shazleen, S. S., Rafiqah, S. A., Harussani, M. M., Kamarudin, S. H., Razman, M. R., et al. (2021). A review on mechanical performance of hybrid natural fiber polymer composites for structural applications. Polymers, 13(13), 2170.
Ahead of Print Subscription Original Research
Volume 14
03
Received 01/07/2026
Accepted 16/07/2026
Published 23/07/2026
Publication Time 22 Days

Login

My IP

PlumX Metrics