Artificial Intelligence Techniques for Smart Polymer Nanocomposite Materials and Industrial Applications

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

S. Gayathri,

D. Kanchana,

T.M. Thiyagu,

Talluri Upender,

N. Bala Sundara Ganapathy,

E. Sabitha,

R. Balasubramaniyan,

  1. Assistant Professor, Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
  2. Assistant Professor, Department of Computer Applications, SRM Institute of Science and Technology, Ramapuram campus, Chennai, Tamil Nadu, India
  3. Associate Professor, Department of Computer Science and Engineering (AIML), Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India
  4. Assistant Professor, Department of Computer Science and Engineering, CMR College of Engineering & Technology, Kandlakoya, Medchal, Hyderabad, Telangana, India
  5. Professor, Department of Information Technology, Panimalar Engineering College, Chennai, Tamil Nadu, India
  6. Assistant Professor, Department of Computer Science and Business Systems, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, Tamil Nadu, India
  7. Associate Professor, Department of Artificial Intelligence and Data Science, Jeppiaar Institute of Technology, Kunnam, Sunguvarchatram, Sriperumbudur T.K Kancheepuram, Tamil Nadu, India

Abstract

Protecting sensitive material data, manufacturing processes, and intelligent monitoring platforms is essential for the fast development of innovative polymer nanocomposite systems in fields such as aerospace, medicine, electronics, automobiles, and energy. In order to safeguard, consistently enhance, and optimize distributed industrial systems that consist of polymer nanocomposite materials, this study presents an AI-driven cybersecurity and cloud computing architecture. The suggested solution employs artificial intelligence (AI), machine learning (ML), cloud computing, edge computing, and real-time cybersecurity to identify security holes, anomalies, and threats to vital databases and production networks. By combining predictive analytics with behavioral monitoring, we can ensure the security of data, resilience of systems, and communication between sensors, smart devices, and cloud servers. Smart manufacturing ecosystems use encrypted cloud connection protocols and blockchain-assisted authentication to protect data. Improved decision-making in automated polymer nanocomposite production systems, faster response times to cyberattacks, and lower operational risks are all outcomes of AI-powered threat identification. For the purpose of material characterization and process optimization, cloud computing provides scalable storage, remote access, and robust computational analysis. The findings show that in dynamic industrial environments, system adaptability, resource efficiency, network security, and penetration rates are all greatly enhanced. The proposed architecture safeguards and expands smart manufacturing, digital transformation, and Industry 4.0 infrastructures made of advanced polymer nanocomposite. Here, we build cyber-physical environments that are safe, smart, and long-lasting for advanced material systems of the future.

Keywords: Artificial Intelligence; Cybersecurity; Cloud Computing; Polymer Nanocomposites; Machine Learning; Edge Computing; Smart Manufacturing; Industry 4.0.

How to cite this article:
S. Gayathri, D. Kanchana, T.M. Thiyagu, Talluri Upender, N. Bala Sundara Ganapathy, E. Sabitha, R. Balasubramaniyan. Artificial Intelligence Techniques for Smart Polymer Nanocomposite Materials and Industrial Applications. Journal of Polymer & Composites. 2026; 14(03):-.
How to cite this URL:
S. Gayathri, D. Kanchana, T.M. Thiyagu, Talluri Upender, N. Bala Sundara Ganapathy, E. Sabitha, R. Balasubramaniyan. Artificial Intelligence Techniques for Smart Polymer Nanocomposite Materials and Industrial Applications. Journal of Polymer & Composites. 2026; 14(03):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=250368

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Ahead of Print Subscription Original Research
Volume 14
03
Received 02/07/2026
Accepted 17/06/2026
Published 21/07/2026
Publication Time 19 Days

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