Measuring Microstructure, Wear Resistance, and Mechanical Reliability Enhancement in Polymer Nanocomposites via Data-Driven Analysis with Deep Learning

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Year : 2026 | Volume : 14 | 04 | Page :
By

P. Kumaresan,

Kalaipriya Omprakash,

Tupili Sangeetha,

Lakshmi Sridevi,

N. Kavitha,

W. Nancy,

R. Madonna Arieth,

  1. Assistant Professor, Department of Electrical and Electronics Engineering, Adhi College of Engineering and Technology, Kanchipuram, Tamil Nadu, India
  2. Assistant Professor, Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, OMR, Tamil Nadu, India
  3. Associate Professor, Department of Computer Science and Engineering, R.M.D Engineering College, Kavaraipettai, Tamil Nadu, India
  4. Professor, Department of Computer Science and Engineering, Chennai Institute of Technology, Chennai, Tamil Nadu, India
  5. Assistant Professor, Department of Electronics and Communication Engineering, Easwari Engineering College, Ramapuram, Chennai, Tamil Nadu, India
  6. Assistant Professor, Department of Electronics and Communication Engineering, Jeppiaar Institute of Technology, Tamil Nadu, India
  7. Associate Professor, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India

Abstract

Polymer nanocomposites have gained great attention owing to their superior mechanical performance, better wear resistance and customizable microstructural properties for aerospace, automotive, medicinal and industrial engineering applications. However, the correct evaluation of the link between the microstructure evolution and the material reliability is a huge issue due to the intricacy of nanoscale interactions and diverse material characteristics. In this study, we propose a data-driven approach that integrates deep learning and advanced microstructural analysis for assessment of wear resistance and mechanical reliability of polymer nanocomposites. The suggested technique is trained using experimental and simulated datasets of microstructural pictures, hardness, tensile strength, fracture toughness, and wear-rate parameters. The architecture is built on a convolutional neural network for the automatic extraction of discriminative microstructural information and the mapping of the non-linear correlations between structural morphology and performance measures. The system can anticipate wear behavior, mechanical failure and dependability correctly under different loads and environmental conditions, while minimizing computational complexity and manual inspection requirements. The deep learning model presents improved prediction accuracy and better generalization performance compared with typical machine learning approaches, which provides a viable approach for trustworthy material characterization and intelligent quality evaluation. The suggested methodology offers fast, accurate and scalable measurement of structural integrity and hence facilitates expedited material development, predictive maintenance and optimal nanocomposite design. Therefore, our work adds to the intelligent engineering of materials, using automated data-driven decision making, for next-generation high performance polymer nanocomposites.

Keywords: Microstructure Characterization, Mechanical Reliability, Convolutional Neural Networks, Predictive Material Modeling, Intelligent Materials Engineering.

How to cite this article: P. Kumaresan, Kalaipriya Omprakash, Tupili Sangeetha, Lakshmi Sridevi, N. Kavitha, W. Nancy, R. Madonna Arieth. Measuring Microstructure, Wear Resistance, and Mechanical Reliability Enhancement in Polymer Nanocomposites via Data-Driven Analysis with Deep Learning. Journal of Polymer & Composites. 2026; 14(04):-.
How to cite this URL: P. Kumaresan, Kalaipriya Omprakash, Tupili Sangeetha, Lakshmi Sridevi, N. Kavitha, W. Nancy, R. Madonna Arieth. Measuring Microstructure, Wear Resistance, and Mechanical Reliability Enhancement in Polymer Nanocomposites via Data-Driven Analysis with Deep Learning. Journal of Polymer & Composites. 2026; 14(04):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=253785

References

  1. T. Admase, S. W. Fanta, N. G. Habtu et al., “The role of advanced fillers in enhancing thermal, chemical and electromechanical properties of polymer composites: A review,” Discov. Mater., vol. 6, p. 220, 2026.
  2. A. Musa, A. Bello, S. M. Adams, A. P. Onwualu, V. C. Anye, K. A. Bello, and I. I. Obianyo, “Nano-enhanced polymer composite materials: A review of current advancements and challenges,” Polymers, vol. 17, no. 7, p. 893, 2025.
  3. K. Singh, S. K. Palaniappan, G. Arora et al., “Recent advances in applications of nanocomposites: A brief overview,” Discov. Mater., vol. 6, p. 58, 2026.
  4. M. Rahman, K. H. Khan, M. M. H. Parvez, N. Irizarry, and M. N. Uddin, “Polymer nanocomposites with optimized nanoparticle dispersion and enhanced functionalities for industrial applications,” Processes, vol. 13, no. 4, p. 994, 2025.
  5. Dwivedi, S. Devi, U. Siddiqui et al., “Investigation of real-time stress-induced self-organization of nanofillers in stretchable nanocomposites: State of the art and future perspectives,” J. Mater. Sci.: Compos., vol. 7, p. 4, 2026.
  6. Hozdić and E. Hozdić, “Mechanical parameters and microstructural evolution of FDM-printed PLA and PLA+CF under variable infill architecture and lubricant exposure,” Polymers, vol. 18, no. 1, p. 72, 2026.
  7. Alqaheem and A. A. Alomair, “Microscopy and spectroscopy techniques for characterization of polymeric membranes,” Membranes, vol. 10, no. 2, p. 33, 2020.
  8. Abdelal, C.-W. Chan, and S. McLoone, “Critical review of recent advances in AI-enhanced SEM and EDS techniques for metallic microstructure characterization,” Appl. Sci., vol. 16, no. 2, p. 975, 2026.
  9. Abdollahi-Mamoudan, C. Ibarra-Castanedo, and X. P. V. Maldague, “Non-destructive testing and evaluation of hybrid and advanced structures: A comprehensive review of methods, applications, and emerging trends,” Sensors, vol. 25, no. 12, p. 3635, 2025.
  10. J. S. Kumar, S. Rajanna, K. N. Krishnamurthy, B. N. A. Deep, A. Razak, A. E. Anqi, A. A. Rajhi, and A. F. Emma, “Wear behaviour and statistical assessment of organomodified nanoclay reinforced glass fiber epoxy nanocomposites,” Sci. Rep., vol. 16, no. 1, p. 3722, 2025.
  11. Antony Jose, Z. Lapierre, T. Williams, C. Hope, T. Jardin, R. Rodriguez, and P. L. Menezes, “Wear- and corrosion-resistant coatings for extreme environments: Advances, challenges, and future perspectives,” Coatings, vol. 15, no. 8, p. 878, 2025.
  12. K. Sharma, S. Gajević, L. K. Sharma, Y. Sharma, M. Sharma, L. Ivanović, S. Milojević, and B. Stojanović, “Self-healing polymer nanocomposites: Mechanisms, structure-property relationships, and emerging applications,” Polymers, vol. 18, no. 2, p. 276, 2026.
  13. Antunes and D. Arencón, “Recent developments in the mechanical behavior of polymer-based composites,” Polymers, vol. 18, no. 5, p. 598, 2026.
  14. D. Mienye and T. G. Swart, “A comprehensive review of deep learning: Architectures, recent advances, and applications,” Information, vol. 15, no. 12, p. 755, 2024.
  15. Ł. Łach and D. Svyetlichnyy, “Advanced numerical modeling of powder bed fusion: From physics-based simulations to AI-augmented digital twins,” Materials, vol. 19, no. 2, p. 426, 2026.
  16. Choudhary, B. DeCost, C. Chen et al., “Recent advances and applications of deep learning methods in materials science,” npj Comput. Mater., vol. 8, p. 59, 2022.
  17. Long, Q. Pang, Y. Deng, X. Pang, Y. Zhang, R. Yang, and C. Zhou, “Recent progress of artificial intelligence application in polymer materials,” Polymers, vol. 17, no. 12, p. 1667, 2025.
  18. A. Musa, A. Bello, S. M. Adams, A. P. Onwualu, V. C. Anye, K. A. Bello, and I. I. Obianyo, “Nano-enhanced polymer composite materials: A review of current advancements and challenges,” Polymers, vol. 17, no. 7, p. 893, 2025.
  19. K. Sharma, S. Miladinović, L. K. Sharma, S. Gajević, Y. Sharma, M. Sharma, S. Čukić, and B. Stojanović, “Graphene/CNT nanocomposites: Processing, properties, and applications,” Nanomaterials, vol. 16, no. 2, p. 100, 2026.
  20. K. Hamzat, M. S. Murad, I. A. Adediran et al., “Fiber-reinforced composites for aerospace, energy, and marine applications: An insight into failure mechanisms under chemical, thermal, oxidative, and mechanical load conditions,” Adv. Compos. Hybrid Mater., vol. 8, p. 152, 2025.
  21. K. Sharma, L. K. Sharma, R. Pradhan, Y. Sharma, M. Sharma, S. Gajević, L. Ivanović, and B. Stojanović, “Interphase-centric and mechanism-driven advances in polymer composites reinforced with nano-, synthetic, and inorganic fillers,” Polymers, vol. 18, no. 3, p. 323, 2026.
  22. D. S. Gowda, H. Raju, V. G. P. Kumar et al., “A review of recent machine learning advances for predicting and optimizing mechanical and tribological properties of fiber-reinforced thermoset polymer nanocomposites,” Discov. Appl. Sci., vol. 8, p. 636, 2026.
  23. Venkateshaiah, V. V. T. Padil, M. Nagalakshmaiah, S. Waclawek, M. Černík, and R. S. Varma, “Microscopic techniques for the analysis of micro and nanostructures of biopolymers and their derivatives,” Polymers, vol. 12, no. 3, p. 512, 2020.
  24. B. Rashid, M. Haque, S. M. M. Islam, and K. M. R. U. Labib, “Nanotechnology-enhanced fiber-reinforced polymer composites: Recent advancements on processing techniques and applications,” Heliyon, vol. 10, no. 2, p. e24692, 2024.
  25. K. Pandey and A. Ganvir, “A systematic review on carbon nanotube- and graphene nanoplatelet-reinforced plasma-sprayed alumina-based hybrid nanocomposite coatings,” ACS Omega, vol. 11, no. 20, pp. 29250–29267, 2026.
  26. Liu, J. Sun, J. Zhao et al., “Hybrid graphene and carbon nanotube–reinforced composites: Polymer, metal, and ceramic matrices,” Adv. Compos. Hybrid Mater., vol. 8, p. 1, 2025.
  27. Demiral, “Strength in adhesion: A multi-mechanics review covering tensile, shear, fracture, fatigue, creep, and impact behavior of polymer bonding in composites,” Polymers, vol. 17, no. 19, p. 2600, 2025.
  28. Pavlovic, L. Valzania, and G. Minak, “Effects of moisture absorption on the mechanical and fatigue properties of natural fiber composites: A review,” Polymers, vol. 17, no. 14, p. 1996, 2025.
  29. Lin and Z. Wang, “Multiscale mechanics and molecular dynamics simulations of the durability of fiber-reinforced polymer composites,” Commun. Mater., vol. 4, p. 66, 2023.
  30. Sharma, G. Arora, M. K. Singh et al., “Review of machine learning approaches for predicting mechanical behavior of composite materials,” Discov. Appl. Sci., vol. 7, p. 1238, 2025.
  31. Lobato, T. Friedrich, and S. Van Aert, “Deep convolutional neural networks to restore single-shot electron microscopy images,” npj Comput. Mater., vol. 10, p. 10, 2024.
  32. Lakshmaiya, P. H. C. Prasad, P. Paramasivam et al., “Tri-domain prediction and optimization of nanocomposite polymers for high-performance 3D printing,” Sci. Rep., vol. 16, p. 19573, 2026.
  33. Long, Q. Pang, Y. Deng, X. Pang, Y. Zhang, R. Yang, and C. Zhou, “Recent progress of artificial intelligence application in polymer materials,” Polymers, vol. 17, no. 12, p. 1667, 2025.
  34. Kokash, M. Kokash, A. Bany-Ata, S. Baqain, and M. Shakoor, “Recent advances in artificial intelligence and machine learning for life cycle-wide additive manufacturing: A comprehensive review,” Machines, vol. 14, no. 5, p. 550, 2026.
  35. Bhanuteja, K. N. Vidyasagar, and N. A. Vinay, “Hybrid convolutional neural network and bidirectional LSTM framework for automated epileptic seizure detection supporting sustainable healthcare,” Discov. Artif. Intell., vol. 6, p. 326, 2026.
  36. Park, G. Choi, S. Yoon et al., “Convergence of soft electronics and artificial intelligence: From materials to intelligent systems,” Nano-Micro Lett., vol. 18, p. 419, 2026.
  37. Srinath, K. Venkateswara Reddy, G. Manohar et al., “Data-driven prediction of microhardness and tensile strength in microwave-sintered ZrC reinforced AA7075/SiC hybrid composites using machine learning,” Sci. Rep., vol. 16, p. 15971, 2026.
Ahead of Print Subscription Original Research
Volume 14
04
Received 13/08/2026
Accepted 27/08/2026
Published 01/09/2026
Publication Time 19 Days

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