Advanced Digital Twin and AI Integration for Real-Time Optimization in Polymer Production

Year : 2025 | Volume : 13 | Issue : 03 | Page : 81 89
    By

    CMAK ZEELAN BASHA,

  • L N K Sai Madupu,

  • Jagini Naga Padmaja,

  • T.S.Rajeswari,

  • D Haritha,

  1. Assistant Professor, Department of Computer Science And Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India
  2. Associate Professor, Department of Civil Engineering, R.V.R. & J.C. College of Engineering, Chowdavaram, Guntur-522019, Andhra Pradesh, India
  3. Assistant Professor, ,Department of Computer Science and Engineering ,Vardhaman College of Engineering ,Shamshabad,Hyderabad-501218, Telangana, India
  4. Assistant Professor, Department of English, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India
  5. Professor, Department of Computer Science And Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India

Abstract

The integration of Internet of Things (IoT) with Artificial Intelligence (AI) technologies opens up considerable avenues for reshaping polymer manufacturing by improving operational effectiveness, securing exceptional product standards, and advancing sustainability in the environment. This academic manuscript delineates an advanced framework that integrates IoT and AI with synergistic technologies, including blockchain, edge computing, and digital twin methodologies, to revolutionize polymer manufacturing processes. The proposed architecture utilizes IoT sensors for the continuous collection of real-time operational data, thereby enabling edge-based AI models to support immediate decision-making. Blockchain technology guarantees robust data security, integrity, and transparency, while digital twins offer virtual representations of physical systems, facilitating precise monitoring and predictive analytics. To enhance the capabilities of this structure, we incorporate federated learning along with reinforcement learning approaches. These AI-driven methodologies foster adaptive and dynamic process optimization while concurrently safeguarding the confidentiality of sensitive industrial information. Sustainability constitutes a fundamental principle of the framework, realized through AI-enabled waste reduction, mitigation of carbon emissions, and the endorsement of circular economy principles to diminish the environmental footprint of polymer manufacturing. Moreover, the system incorporates sophisticated cybersecurity protocols, encompassing anomaly detection mechanisms and zero-trust architectures, to tackle significant challenges concerning data security, interoperability, and system reliability. This innovative and multifaceted strategy strategically equips the polymer manufacturing sector for a future characterized by intelligent, secure, and sustainable production methodologies. By addressing contemporary market exigencies and regulatory stipulations, the proposed framework lays the groundwork for attaining operational excellence, sustainability objectives, and heightened competitiveness within an increasingly dynamic industry landscape.

Keywords: IoT (internet of things), AI (artificial intelligence), blockchain, digital twin, polymer manufacturing, reinforcement learning, federated learning

[This article belongs to Journal of Polymer and Composites ]

How to cite this article:
CMAK ZEELAN BASHA, L N K Sai Madupu, Jagini Naga Padmaja, T.S.Rajeswari, D Haritha. Advanced Digital Twin and AI Integration for Real-Time Optimization in Polymer Production. Journal of Polymer and Composites. 2025; 13(03):81-89.
How to cite this URL:
CMAK ZEELAN BASHA, L N K Sai Madupu, Jagini Naga Padmaja, T.S.Rajeswari, D Haritha. Advanced Digital Twin and AI Integration for Real-Time Optimization in Polymer Production. Journal of Polymer and Composites. 2025; 13(03):81-89. Available from: https://journals.stmjournals.com/jopc/article=2025/view=210765


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Regular Issue Subscription Original Research
Volume 13
Issue 03
Received 21/01/2025
Accepted 01/02/2025
Published 16/04/2025
Publication Time 85 Days
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