STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Polymer & Composites Original Research

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

  1. CMAK Zeelan Basha Department of Computer Science And Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur
  2. L N K Sai Madupu Department of Civil Engineering, R.V.R. & J.C. College of Engineering, Chowdavaram, Guntur-522019
  3. Jagini Naga Padmaja ,Department of Computer Science and Engineering ,Vardhaman College of Engineering ,Shamshabad,Hyderabad-501218
  4. T.S.Rajeswari Department of English, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur
  5. D Haritha Department of Computer Science And Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur

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

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