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

Journal of Polymer & Composites Original Research Special issue

AI-Designed Functionally Graded Polymer Composites for Multifunctional Thin Films

  1. D. Sai Ganesh Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur District
  2. S.N. Padhi Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram
  3. Mamata Choudhury Department of Computer Application, PSCMR college of Engineering and Technology, Vijayawada, Krishna District
  4. K. Vikas Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur District
  5. K. Anudeep Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur District

Abstract

The design of multifunctional polymer composite thin films requires simultaneous optimization of mechanical, optical, barrier, and thermal properties—objectives often in conflict when using conventional homogeneous materials. This study presents an artificial intelligence-driven framework for designing functionally graded material (FGM) architectures in polymer nanocomposite thin films. We integrated machine learning with physics-based modeling to optimize compositional gradients across film thickness, achieving superior performance compared to homogeneous and discrete multilayer alternatives. A neural network trained on 150,000 finite element simulations and experimental data predicted material properties with R²>0.94 accuracy. Bayesian optimization identified optimal gradient profiles for poly(methyl methacrylate) (PMMA) matrices reinforced with titanium dioxide (TiO₂) nanoparticles and functional additives. Experimental validation via layer-by-layer spin coating demonstrated 82% improvement in tensile strength, 92% optical transmittance, 47% reduction in water vapor transmission rate, and enhanced thermal stability compared to homogeneous films. The optimized FGM architecture exhibited gradual composition transitions (40-80 wt% polymer, 15-45 wt% nanoparticles across 100 μm thickness), eliminating interfacial delamination while maintaining processing feasibility. Economic analysis reveals cost-competitiveness ($28/m²) with conventional multilayer films ($22/m²) while delivering 22% higher performance index. This AI-guided approach enables rapid exploration of vast design spaces, reducing development cycles from years to weeks, and establishes a generalizable methodology for multifunctional coating applications in optoelectronics, packaging, and protective systems.

Keywords

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