METAHEURISTIC DESIGN OF EPOXY-BASED POLYMER COMPOSITES

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 | 02 | Page :
    By

    Vishwadeepak,

  • M. Achudhan,

  1. Research Scholar, Department of Mechanical, Bharath Institute of Higher Education and Research, Selaiyur, Chennai, Tamil Nadu, India
  2. Associate Professor, Department of Mechanical, Bharath Institute of Higher Education and Research, Selaiyur, Chennai, Tamil Nadu, India

Abstract

This research investigates the optimization of epoxy resin based polymer compositions in composite materials to enhance their mechanical and tribological properties for industrial applications. We examined the properties of composite materials by running experiments and optimizing these results using Firefly and five optimization algorithms to find the best steel and epoxy ratios. Our tests confirmed that higher dosages of steel powder increased materials’ resistance to breaking and impacts yet lowered their wear values especially in compositions with higher steel powder contents. The Firefly algorithm produced the optimal steel powder and epoxy resin composition at 86.36% steel powder and 13.64% epoxy resin while PSO and MOTLBO achieved a composition of 85% steel powder and 15% epoxy resin. Our Firefly optimization showed ₹4778.29 total cost-effectiveness in industrial use while PSO optimization cost just ₹1042.01. The research demonstrates new ways to use different meta heuristic algorithms for designing composite materials to help manufacturers choose between mechanical strength and lower production costs. Steel powder reinforcement shows improved mechanical performance in epoxy resin polymer composites by strengthening both wear resistance and strength at a cost-effective solution for industrial manufacturing. The study highlights a practical path to design materials that can help improve production in industries with demanding performance needs.

Keywords: Epoxy Polymer Composites, Steel Powder Reinforcement, Metaheuristic Optimization, Mechanical Properties, Tribological Behavior, Firefly Algorithm, Particle Swarm Optimization (PSO), Epoxy Resin Matrix, Polymer-Based Materials, Composite Design.

How to cite this article:
Vishwadeepak, M. Achudhan. METAHEURISTIC DESIGN OF EPOXY-BASED POLYMER COMPOSITES. Journal of Polymer & Composites. 2026; 14(02):-.
How to cite this URL:
Vishwadeepak, M. Achudhan. METAHEURISTIC DESIGN OF EPOXY-BASED POLYMER COMPOSITES. Journal of Polymer & Composites. 2026; 14(02):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=239918


References

  1. Bajpai A, Saxena P, Kunze K. Tribo-mechanical characterization of carbon fiber-reinforced cyanate ester resins modified with fillers. Polymers. 2020 Jul 31;12(8):1725.
  2. Ayoola WA, Onoh CO, Durowaye SI, Yusuf AA, Bankole RO, Omoniyi EO, Sanni AA. STUDY OF THE PHYSICAL, MECHANICAL, CHEMICAL AND THERMAL CHARACTERISTICS OF PHENOLIC RESIN-STEEL SLAG REINFORCED COMPOSITES. Platform: A Journal of Engineering. 2022 Jun 30;6(2):2-16.
  3. Budati S, Sulaiman MH, Leman Z, Yusoff MZ, Zainudin ES. Experimental investigation on the physical, mechanical, and tribological behavior of brake friction composites for railway application. Jurnal Tribologi. 2024 Dec;40:212-25.
  4. Xia D, Wang Y, Ren K. Experimental study on the impact resistance of polymer-modified steel fiber-reinforced recycled aggregate concrete. Buildings. 2023 Nov 28;13(12):2965.
  5. Amuthakkannan P, Santhosh W, Saravanan S, Arunprasath K, Ragurajan S. EXPERIMENTAL INVESTIGATION ON MECHANICAL PROPERTIES OF COPPER SLAG POWDER REINFORCED PARTICULATE POLYMER COMPOSITE.
  6. Yang H, Ng BC, Yu HC, Liang HH, Kwok CC, Lai FW. Mechanical properties study on sandwich hybrid metal/(carbon, glass) fiber reinforcement plastic composite sheet. Advanced Composites and Hybrid Materials. 2022 Mar;5(1):83-90.
  7. Asteris PG, Naseri H, Hajihassani M, Kharghani M, Chalioris CE. On the mechanical characteristics of fiber reinforced polymer concrete. Adv. Concr. Constr. 2021 Jan;12(4):271-82.
  8. Mao K, Greenwood D, Ramakrishnan R, Goodship V, Shrouti C, Chetwynd D, Langlois+ P. The wear resistance improvement of fibre reinforced polymer composite gears. Wear. 2019 Apr 30;426:1033-9.
  9. Waller MD, McIntyre SM, Koudela KL. Composite materials for hybrid aerospace gears. Journal of the American Helicopter Society. 2020 Oct 1;65(4):1-1.
  10. Černe B, Bergant Z, Šturm R, Tavčar J, Zorko D. Experimental and numerical analysis of laminated carbon fibre-reinforced polymer gears with implicit model for coefficient-of-friction evaluation. Journal of Computational Design and Engineering. 2022 Feb;9(1):246-62.
  11. Mohsenzadeh R, Soudmand BH, Shelesh-Nezhad K. A combined experimental-numerical approach for life analysis and modeling of polymer-based ternary nanocomposite gears. Tribology International. 2022 Sep 1;173:107654.
  12. Tran-Ngoc H, Khatir S, Ho-Khac H, De Roeck G, Bui-Tien T, Wahab MA. Efficient Artificial neural networks based on a hybrid metaheuristic optimization algorithm for damage detection in laminated composite structures. Composite Structures. 2021 Apr 15;262:113339.
  13. Huang Y, Lei Y, Luo X, Fu C. Prediction of compressive strength of rice husk ash concrete: A comparison of different metaheuristic algorithms for optimizing support vector regression. Case Studies in Construction Materials. 2023 Jul 1;18:e02201.
  14. Khan H, Hussain S, Hussain SF, Gul S, Ahmad A, Ullah S. Multivariate modeling and optimization of Cr (VI) adsorption onto carbonaceous material via response surface models assisted with multiple regression analysis and particle swarm embedded neural network. Environmental Technology & Innovation. 2021 Nov 1;24:101952.
  15. Yang P, Li C, Qiu Y, Huang S, Zhou J. Metaheuristic optimization of random forest for predicting punch shear strength of FRP-reinforced concrete beams. Materials. 2023 May 28;16(11):4034.
  16. Liu X, Qin J, Zhao K, Featherston CA, Kennedy D, Jing Y, Yang G. Design optimization of laminated composite structures using artificial neural network and genetic algorithm. Composite Structures. 2023 Feb 1;305:116500.
  17. Wahab S, Suleiman M, Shabbir F, Mahmoudabadi NS, Waqas S, Herl N, Ahmad A. Predicting confinement effect of carbon fiber reinforced polymers on strength of concrete using metaheuristics-based artificial neural networks. arXiv preprint arXiv:2403.13809. 2023 Dec 22.
  18. Tiwari A. Prediction model for hardness and tensile strength of graphene reinforced AZ 61 alloy based composite using Metaheuristic Algorithm. Global Journal of Engineering and Technology Advances. 2024;20(03):042-52.
  19. Abd Elaziz M, Elsheikh AH, Oliva D, Abualigah L, Lu S, Ewees AA. Advanced Metaheuristic Techniques for Mechanical Design Problems: Review: MA Elaziz et al. Archives of Computational Methods in Engineering. 2022 Jan;29(1):695-716.
  20. Mohanta DK, Sahoo B, Mohanty AM. Optimization of process parameter in AI7075 turning using grey relational, desirability function and metaheuristics. Materials and Manufacturing Processes. 2023 Sep 10;38(12):1615-25.
  21. Kumar Bagal D, Parida B, Barua A, Naik B, Jeet S, Kumar Singh S, Kumar Pattanaik A. Mechanical characterization of hybrid polymer SiC nano composite using hybrid RSM-MOORA-whale optimization algorithm. InIOP conference series: materials science and engineering 2020 Nov 1 (Vol. 970, No. 1, p. 012017). IOP Publishing.
  22. Feng ZK, Niu WJ, Liu S. Cooperation search algorithm: A novel metaheuristic evolutionary intelligence algorithm for numerical optimization and engineering optimization problems. Applied Soft Computing. 2021 Jan 1;98:106734.
  23. Pedrammehr S, Hejazian M, Chalak Qazani MR, Parvaz H, Pakzad S, Ettefagh MM, Suhail AH. Machine learning-based modelling and meta-heuristic-based optimization of specific tool wear and surface roughness in the milling process. Axioms. 2022 Aug 26;11(9):430.
  24. Almeshaal M, Palanisamy S, Murugesan TM, Palaniappan M, Santulli C. Physico-chemical characterization of Grewia Monticola Sond (GMS) fibers for prospective application in biocomposites. Journal of Natural Fibers. 2022 Dec 2;19(17):15276-90.
  25. Santulli, Carlo, Sivasubramanian Palanisamy, and Mayandi Kalimuthu. “Pineapple fibers, their composites and applications.” Plant Fibers, their Composites, and Applications. Woodhead Publishing, 2022. 323-346.
  26. Santulli C, Palanisamy S, Kalimuthu M. Pineapple fibers, their composites and applications. InPlant Fibers, their Composites, and Applications 2022 Jan 1 (pp. 323-346). Woodhead Publishing.
  27. Sumesh KR, Palanisamy S, Khan T, Ajithram A, Ahmed OS. Mechanical, morphological and wear resistance of natural fiber/glass fiber-based polymer composites. BioResources. 2024 Apr 11;19(2):3271-89.
  28. Palanisamy S, Kalimuthu M, Santulli C, Palaniappan M, Nagarajan R, Fragassa C. Tailoring epoxy composites with Acacia caesia bark fibers: Evaluating the effects of fiber amount and length on material characteristics. Fibers. 2023 Jul 17;11(7):63.Palaniappan M, Palanisamy S, Murugesan TM, Alrasheedi NH, Ataya S, Tadepalli S, Elfar AA. Novel Ficus retusa L. aerial root fiber: a sustainable alternative for synthetic fibres in polymer composites reinforcement. Biomass Conversion and Biorefinery. 2025 Mar;15(5):7585-601.
Ahead of Print Subscription Original Research
Volume 14
02
Received 20/06/2025
Accepted 06/08/2025
Published 09/04/2026
Publication Time 293 Days
57

Login


My IP

PlumX Metrics