AI-Driven Inverse Design of Functionally Graded Bio-Nanocomposites for Sustainable High-Barrier Packaging

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

    Tanya Chouhan,

  • Chinhita Sanyal,

  • Ritam Rajak,

  • Indrajit Ghosal,

  1. Assistant Professor, Department of Management Studies, Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, India
  2. Assistant Professor, Amity business school, Amity University, Kolkata, W, India
  3. Assistant Professor, Department of Computer Science and Engineering – (AI&ML), Moodlakatte Institute of Technology, Kundapura, Karnataka, West Bengal, India
  4. Associate Professor, Department of Management, Brainware University, Kolkata, West Bengal, India

Abstract

Multilayer plastic packaging realizes high barrier performance through laminated heterogeneous structures, but the heterogeneous structure has severe end-of-life challenges caused by the interfacial incompatibility of materials and the poor recyclability. This study proposes the inverse design of functionally graded PLA-nanoclay composite films by reinforcement learning as a monolithic alternative to traditional multilayer systems. Twin-screw extrusion is designed as a continuous control Markov decision process, and proximal policy optimization (PPO) is implemented to arrive at an ideal spatial distribution of nanoclay under the coupling of oxygen barrier efficiency and mechanical stiffness. Model predictions suggest that the optimized reinforcement profile moves to surface-enriched gradient form with more nanoclay near external interfaces, and less concentrated in the core region. This type of graded architecture is predicted to reduce oxygen permeability by ~55-65% compared to neat PLA, as well as offer a further ~12-18% reduction compared to uniformly dispersed composites with comparable average filler loading. At the same time, stiffness amplification is controlled by the matrix-dominated core. In terms of monolithic graded products, a comparative lifecycle cost analysis indicates that monolithic graded architecture can decrease total lifecycle costs by eliminating the elimination of end-of-life disposal penalty and by ensuring the value recovery of the material. Although the results are obtained without experimental validation of the models by using established models of permeability and micromechanics, the results illustrate the potential of reinforcement learning for spatially optimized composite design under coupled performance and economic constraints.

Keywords: Deep Reinforcement Learning; Functionally Graded Materials; Bio-Nanocomposites; Circular Economy; Gas Barrier Properties; Inverse Design.

How to cite this article:
Tanya Chouhan, Chinhita Sanyal, Ritam Rajak, Indrajit Ghosal. AI-Driven Inverse Design of Functionally Graded Bio-Nanocomposites for Sustainable High-Barrier Packaging. Journal of Polymer & Composites. 2026; 14(03):-.
How to cite this URL:
Tanya Chouhan, Chinhita Sanyal, Ritam Rajak, Indrajit Ghosal. AI-Driven Inverse Design of Functionally Graded Bio-Nanocomposites for Sustainable High-Barrier Packaging. Journal of Polymer & Composites. 2026; 14(03):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=242921


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Ahead of Print Subscription Original Research
Volume 14
03
Received 24/02/2026
Accepted 07/04/2026
Published 05/05/2026
Publication Time 70 Days
71

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