AI-Enabled Optimization of Additively Manufactured Composite Materials for Enhanced Mechanical and Thermal Performance

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Year : 2026 | Volume : 14 | 02 | Page :
    By

    G. Nagaraj,

  • P. Girish,

  • Pallavi Hallappanavar Basavaraja,

  • Anusha Preetham,

  • G. Anil Kumar,

  • D. Gouse Peera,

  1. Associate Professor, Department of Mechanical Engineering, Sethu Institute of Technology, Virudhunagar District, Tamil Nadu, India
  2. Assistant Professor, Department of Civil Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, Karnataka, India
  3. Associate Professor, Department of Information Science and Engineering, Academy of Technology and Management, Bangalore, Karnataka, India
  4. Associate Professor, Department of Computer Science and Engineering, Dayanand Sagar College of Engineering, Bangalore, Karnataka, India
  5. Assistant Professor, Department of Physics, Sreenidhi Institute of Science and Technology, Jawaharlal Nehru Technological University, Hyderabad, Telangana, India
  6. Assistant Professor, Department of Civil Engineering, Annamacharya University, Rajampet, Andhra Pradesh, India

Abstract

This paper discusses the optimization of multi-objective optimization of enhanced coupling of heat and mechanical properties of 3D printed polymer composite materials by artificial intelligence (AI), as a component of a multi-objective optimization framework. It aims at development of nonlinear printing parameters and material properties relationships to achieve maximum tensile strength and thermal conductivity in polymer composites produced through fused deposition modeling (FDM). Short carbon fiber reinforcement was used to make the polymer composite by FDM at various deposition temperatures, raster pattern, and infill densities. A machine-learning model was created experimentally with the tensile strength and thermal conductivity of the polymer composite measured and then combined with an evolutionary optimization algorithm to give a combination of printing parameters, which will give the optimum structural and thermal performance of the final part. The optimized composite specimens exhibited an average tensile strength improvement of approximately 9.4% compared to baseline systems reported in recent literature. Similarly, effective thermal conductivity increased by nearly 14.2%, indicating improved heat transfer pathways within the composite matrix. The predictive model achieved a coefficient of determination of 0.94 for tensile strength and 0.91 for thermal conductivity, demonstrating strong agreement between predicted and experimental responses. The suggested structure will allow the simultaneous enhancement of mechanical and thermal performance of additive-manufactured polymer composite structures, and, potentially, intelligent process-driven design of multifunctional composite elements.

Keywords: Additive Manufacturing, Polymer Composite Materials, Thermo-Mechanical Performance, AI-Based Process Optimization, Fused Deposition Modeling.

How to cite this article:
G. Nagaraj, P. Girish, Pallavi Hallappanavar Basavaraja, Anusha Preetham, G. Anil Kumar, D. Gouse Peera. AI-Enabled Optimization of Additively Manufactured Composite Materials for Enhanced Mechanical and Thermal Performance. Journal of Polymer & Composites. 2026; 14(02):-.
How to cite this URL:
G. Nagaraj, P. Girish, Pallavi Hallappanavar Basavaraja, Anusha Preetham, G. Anil Kumar, D. Gouse Peera. AI-Enabled Optimization of Additively Manufactured Composite Materials for Enhanced Mechanical and Thermal Performance. Journal of Polymer & Composites. 2026; 14(02):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=240336


References

  1. K. S. Raja et al., “Sustainable manufacturing of FDM-manufactured composite impellers using hybrid machine learning and simulation-based optimization,” Materials science in additive manufacturing, vol. 4, no. 3, p. 025200033, Jul. 2025, doi: 10.36922/msam025200033.
  2. K. Thanikonda, J. K. Rao, and D. K. Vaka, “3D Printed Composite Materials: Innovations In Additive Manufacturing And Mechanical Performance,” Jan. 2024, doi: 10.53555/kuey.v30i10.8075.
  3. Choi, R. C. Advíncula, H. Wu, and Y. Jiang, “Artificial intelligence and machine learning in the design and additive manufacturing of responsive composites,” MRS Communications, vol. 13, no. 5, pp. 714–724, Sep. 2023, doi: 10.1557/s43579-023-00473-9.
  4. H. Dhage and N. Khedkar, “Predictive machine learning and printing parameter optimization for enhanced impact performance of 3D-printed Onyx-Kevlar composites,” Discover materials, vol. 5, no. 1, Aug. 2025, doi: 10.1007/s43939-025-00307-6.
  5. R. Ragunath, K. Radha, T. Mohanraj, and C. Venkatachalam, “Q-Learning Driven Metaheuristic for Tailoring the Solid State Properties of Carbon Nanotube-Graphene Nanocomposites for Automotive and Aerospace Applications,” ECS Journal of Solid State Science and Technology, Jul. 2025, doi: 10.1149/2162-8777/adf3e5.
  6. Rooney, Y. Dong, A. K. Basak, and A. Pramanik, “Prediction of Mechanical Properties of 3D Printed Particle-Reinforced Resin Composites,” Journal of composites science, vol. 8, no. 10, p. 416, Oct. 2024, doi: 10.3390/jcs8100416.
  7. Özsoy and B. Aksoy, “Artificial Intelligence and Machine Learning in Additive Manufacturing,” Advances in computational intelligence and robotics book series, pp. 255–284, Aug. 2025, doi: 10.4018/979-8-3373-0746-6.ch009.
  8. Jiang, A. X. Serrano, W. Choi, R. C. Advíncula, and H. F. Wu, “Advanced and functional composite materials via additive manufacturing: Trends and perspectives,” MRS Communications, vol. 14, no. 4, pp. 449–459, Aug. 2024, doi: 10.1557/s43579-024-00625-5.
  9. Mamodiya, I. Kishor, M. Amin, A. Alqutaish, G. Alradwan, and M. Obiedate, “Hybrid soft computing and adaptive learning strategies for intelligent autonomous systems,” International Journal of Data and Network Science, vol. 10, pp. –, 2026, doi: 10.5267/j.ijdns.2026.1.010.
  10. Lavanya, “Enhancement of Composite Materials Using 3D Printing Technology,” International Journal For Science Technology And Engineering, vol. 12, no. 12, pp. 1314–1327, Dec. 2024, doi: 10.22214/ijraset.2024.66010.
  11. Okolo, “Investigation of the influence of AI-controlled process parameter adjustment on the mechanical properties of LBPF-manufactured parts,” Materials research proceedings, vol. 54, pp. 218–227, Jan. 2025, doi: 10.21741/9781644903599-24.
  12. Xu et al., “AI‐Powered Inverse Design of High‐Performance Unidirectional CFRP Composites: Breaking the Compressive‐Tensile Trade‐Off via Machine Learning,” Macromolecular Rapid Communications, Sep. 2025, doi: 10.1002/marc.202500482.
  13. Yu. Stepanov et al., “Optimization of 3D Printing Parameters of High Viscosity PEEK/30GF Composites,” Polymers, vol. 16, no. 18, p. 2601, Sep. 2024, doi: 10.3390/polym16182601.
  14. Kibrete, T. Trzepieciński, H. S. Gebremedhen, and D. E. Woldemichael, “Artificial Intelligence in Predicting Mechanical Properties of Composite Materials,” Journal of composites science, Sep. 2023, doi: 10.3390/jcs7090364.
  15. AI-Enhanced Additive Manufacturing: Intelligent 3d Printing for Complex Designs,” Journal of Primeasia, vol. 5, no. 1, pp. 1–9, Jan. 2024, doi: 10.25163/primeasia.5110247.
  16. G. Udu, N. Osa-uwagboe, O. A. Adeniran, A. Aremu, and M. G. Khaksar, “A machine learning approach to characterise fabrication porosity effects on the mechanical properties of additively manufactured thermoplastic composites,” Journal of Reinforced Plastics and Composites, Mar. 2024, doi: 10.1177/07316844241236696.
  17. Liu et al., “Neural Co-Optimization of Structural Topology, Manufacturable Layers, and Path Orientations for Fiber-Reinforced Composites,” ACM Transactions on Graphics, vol. 44, no. 4, pp. 1–17, Jul. 2025, doi: 10.1145/3730922.
  18. Li, W. Yao, Y. Lu, J. Chen, Y. Sun, and X. Hu, “High fidelity FEM based on deep learning for arbitrary composite material structure,” Composite Structures, May 2024, doi: 10.1016/j.compstruct.2024.118176.
  19. Zhu, E. A. Bamidele, X. Shen, G. Zhu, and B. Li, “Machine Learning Aided Design and Optimization of Thermal Metamaterials.,” Chemical Reviews, Mar. 2024, doi: 10.1021/acs.chemrev.3c00708.
  20. H. Zubayer, Y. Xiong, Y. Wang, and H. M. Imdadul, “Enhancing additive manufacturing precision: intelligent inspection and optimization for defect-free continuous carbon fibre-reinforced polymer,” Composites Part C: Open Access, Mar. 2024, doi: 10.1016/j.jcomc.2024.100451.
  21. Thakur, R. Kumar, R. Kumar, R. Singh, and V. Kumar, “Hybrid additive manufacturing of highly sustainable Polylactic acid -Carbon Fiber-Polylactic acid sandwiched composite structures: Optimization and machine learning,” Journal of Thermoplastic Composite Materials, p. 089270572311801, May 2023, doi: 10.1177/08927057231180186.
  22. D. Dubey and K. Debnath, “Mechanical, thermal and morphological analysis of 3D-printed woven glass fiber-reinforced polylactic acid composites using TLBO and ANN,” Rapid Prototyping Journal, Aug. 2025, doi: 10.1108/rpj-10-2024-0426.
  23. Nawafleh and F. Al-Oqla, “Evaluation of mechanical properties of fiber-reinforced syntactic foam thermoset composites: A robust artificial intelligence modeling approach for improved accuracy with little datasets,” Journal of the mechanical behavior of materials, vol. 32, no. 1, Jan. 2023, doi: 10.1515/jmbm-2022-0285.
  24. J. Golden Renjith Nimal, R. Sangamaeswaran, R. Hariharan, S. R. Divahar, S. S. Tamil Selvi, and S. Ebinraj, “AI-Driven Material Design and Discovery: Revolutionizing the Development of Novel Alloys and Composites,” pp. 113–118, Aug. 2025, doi: 10.1109/roma66616.2025.11155751.
  25. Barhemat, S. Mahjoubi, W. Meng, and Y. Bao, “AI-assisted Design, 3d Printing, and Evaluation of Architecture Polymer-concrete Composites (APCC) With High Specific Flexural Strength and High Specific Toughness,” Social Science Research Network, Jan. 2023, doi: 10.2139/ssrn.4515439.
  26. M. H. Truong, H. N. T. Trung, T. Mai, T. Mai, V. T. Hoang, and Q.-P. Ma, “Physics-informed machine learning for multi-objective optimization in additive manufacturing: a data-efficient approach,” MM Science Journal, vol. 2025, no. 4, Sep. 2025, doi: 10.17973/mmsj.2025_10_2025094.
  27. Badini, S. Regondi, and R. Pugliese, “Unleashing the Power of Artificial Intelligence in Materials Design.,” Materials, vol. 16, no. 17, Aug. 2023, doi: 10.3390/ma16175927.
  28. Shin, R. Alberdi, R. A. Lebensohn, and R. Dingreville, “A deep material network approach for predicting the thermomechanical response of composites,” Composites Part B-engineering, vol. 272, p. 111177, Mar. 2024, doi: 10.1016/j.compositesb.2023.111177.
  29. S. Ahmad, P. Goyal, and D. Dwivedi, “Harnessing unsupervised machine learning for advanced nanodevice fabrication,” in Enhancing Hybrid Nanodevice Fabrication Efficiency Using Machine Learning, U. Mamodiya, S. L. Tripathi, D. Ghai, and D. K. Jain, Eds. Hoboken, NJ, USA: Wiley, 2026, ch. 7, doi:10.1002/9781394355310.ch7.
  30. Q. Huang, “Integrating Deep Learning with Generative Design and Topology Optimization for Efficient Additive Manufacturing,” Applied and Computational Engineering, vol. 116, no. 1, pp. 168–173, Nov. 2024, doi: 10.54254/2755-2721/116/20251723.
  31. Ghimire and A. Raji, “Use of Artificial Intelligence in Design, Development, Additive Manufacturing, and Certification of Multifunctional Composites for Aircraft, Drones, and Spacecraft,” Applied Sciences, Jan. 2024, doi: 10.3390/app14031187.
  32. Yu, J. Griffis, G. Manogharan, and A. Panesar, “Multi-material additive manufacturing: a computational design perspective,” Virtual and Physical Prototyping, vol. 20, no. 1, Aug. 2025, doi: 10.1080/17452759.2025.2546671.
  33. Kishor, U. Mamodiya, S. S. Ahmad, P. Goyal, and D. Dwivedi, “Enabling smarter nanosystems,” in Enhancing Hybrid Nanodevice Fabrication Efficiency Using Machine Learning, U. Mamodiya, S. L. Tripathi, D. Ghai, and D. K. Jain, Eds. Hoboken, NJ, USA: Wiley, 2026, ch. 6, doi:10.1002/9781394355310.ch6.
  34. Fang, Y. Li, X. Zhao, and J. Chen, “Evaluation of Tensile Properties of 3D-Printed PA12 Composites with Short Carbon Fiber Reinforcement: Experimental and Ma-Chine Learning-Based Predictive Modelling,” Journal of composites science, vol. 9, no. 9, p. 461, Sep. 2025, doi: 10.3390/jcs9090461.
  35. Kakanuru, P. Terway, N. K. Jha, and K. Pochiraju, “Process–Material–Performance Trade-off Exploration of Materials Sintering with Machine Learning Models,” Integrating materials and manufacturing innovation, Nov. 2024, doi: 10.1007/s40192-024-00380-4.
  36. P. Teimouri, M. Alinia, S. Kamarian, S. Saber-Samandari, G. Li, and J. Song, “Design optimization of additively manufactured sandwich beams through experimentation, machine learning, and imperialist competitive algorithm,” Journal of Engineering Design, Feb. 2024, doi: 10.1080/09544828.2024.2309862.
  37. Park, J. Lee, K. Park, and S. Ryu, “HGNet: A Hierarchical Multi‐Task Learning Approach for Accelerated Composite Material Design and Discovery,” Advanced Engineering Materials, Sep. 2023, doi: 10.1002/adem.202300867.
Ahead of Print Subscription Original Research
Volume 14
02
Received 24/02/2026
Accepted 16/03/2026
Published 20/04/2026
Publication Time 55 Days
20

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