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

Journal of Polymer & Composites Original Research

AI-Driven Optimization of Biopolymer Composite Formulations Using IoT Data Streams

  1. Praveena Murala Department of Computer Science and Engineering, QIS College of Engineering and Technology
  2. Pedapati Veerababu Department of CSE – Data Science, Vignan Institute of Technology and Science, Hyderabad
  3. Shailaja Mantha Department of Electronics and Communication Engineering, Sreenidhi Institute of Science and Technology, Hyderabad
  4. Subarno Bhattacharyya Office of Digital Learning and Online Education, O.P. Jindal Global University, Sonipat
  5. Reshma V. K Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore
  6. C. M. Sheela Rani Department of Computer Science and Engineering, K L University, Vaddeswaram, Guntur District

Abstract

Biodegradable polymer composites have emerged as a sustainable alternative to petroleum-based materials in packaging, biomedical, and structural applications. However, traditional formulation techniques for reinforced polymer composites often lack precision and fail to adapt to real-time variations during processing, resulting in suboptimal material performance. This research proposes a real-time AI-IoT-enabled framework to optimize biopolymer composite formulations. The goal is to intelligently tune composite properties such as mechanical strength, moisture resistance, and biodegradation behavior by leveraging continuous sensor data and machine learning. A hybrid prediction model integrating Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM) with Extreme Gradient Boosting (XGBoost) was developed to predict composite behavior from IoT-based fabrication data. Key parameters polymer-filler ratio, temperature, humidity, and curing time were collected using an embedded sensor network during the composite processing stage. A Multi-Objective Genetic Algorithm (MOGA) was employed to optimize formulation targets across multiple property dimensions. Experimental validation was conducted using PLA-starch and PHA-lignin biopolymer composites. The proposed system achieved high predictive accuracy (R² > 0.90) across all polymer composite properties. Optimized reinforced formulations resulted in a 17.8% improvement in tensile strength and a 22.1% reduction in water absorption, while maintaining biodegradation above 90%. Experimental outcomes closely matched model predictions with less than 5% deviation. This study demonstrates a novel AI-driven optimization platform for biodegradable polymer composite formulations, offering a closed-loop, scalable solution for intelligent material design. The framework enables real-time formulation control for reinforced polymer systems, bridging performance, sustainability, and smart manufacturing.

Keywords

References (28)

  1. Rooney K, Dong Y, Basak AK, Pramanik A. Prediction of Mechanical Properties of 3D Printed Particle-Reinforced Resin Composites. Journal of Composites Science. 2024;8(10):416. doi:10.3390/jcs8100416
  2. Sharma A, Mukhopadhyay T, Rangappa SM, Siengchin S, Kushvaha V. Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design. Archives of Computational Methods in Engineering. 2022;29(5):3341-3385. doi:10.1007/s11831-021-09700-9
  3. Champa-Bujaico E, García-Díaz P, Díez-Pascual AM. Machine Learning for Property Prediction and Optimization of Polymeric Nanocomposites: A State-of-the-Art. International Journal of Molecular Sciences. 2022;23(18):10712. doi:10.3390/ijms231810712
  4. Mamodiya U, Kishor I. A Comparative Study on the Performance of DualAxis Solar Tracking Systems and Fixed Solar Arrays. Proceedings of the 5th International Conference on Information Management & Machine Intelligence. 2023:1-4. doi:10.1145/3647444.3647943
  5. Nawafleh N, AL-Oqla FM. Artificial neural network for predicting the mechanical performance of additive manufacturing thermoset carbon fiber composite materials. Journal of the Mechanical Behavior of Materials. 2022;31(1):501-513. doi:10.1515/jmbm-2022-0054
  6. Pugar JA, Gang C, Huang C, Haider KW, Washburn NR. Predicting Young’s Modulus of Linear Polyurethane and Polyurethane–Polyurea Elastomers: Bridging Length Scales with Physicochemical Modeling and Machine Learning. ACS Applied Materials & Interfaces. 2022;14(14):16568-16581. doi:10.1021/acsami.1c24715
  7. Sunori SK, Arora S, Agarwal P, Mittal A, Mamodiya U, Juneja P. SA based Optimization of Controller Parameters for Crystallization Unit of Sugar Factory. 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI). 2022:341-346. doi:10.1109/icoei53556.2022.9776904
  8. Moumen A, Lakhdar A, Laabid Z, Mansouri K. Towards smart modeling of mechanical properties of a bio composite based on a machine learning. International Journal of Electrical and Computer Engineering (IJECE). 2022;12(3):3138. doi:10.11591/ijece.v12i3.pp3138-3145
  9. Baturynska I. Application of Machine Learning Techniques to Predict the Mechanical Properties of Polyamide 2200 (PA12) in Additive Manufacturing. Applied Sciences. 2019;9(6):1060. doi:10.3390/app9061060
  10. Isabona, L. L. Ibitome, A. L. Imoize, U. Mamodiya, A. Kumar, M. M. Hassan, et al., "Statistical characterization and modeling of radio frequency signal propagation in mobile broadband cellular next generation wireless networks", Computational Intelligence and Neuroscience, vol. 2023, no. 1, pp. 5236566, 2023.
  11. Kishor I. A prototype framework for customizing virtual reality interactions for paralyzed patients integrating physical and rehabilitation modalities. Recent Advances in Sciences, Engineering, Information Technology & Management. 2024:338-344. doi:10.1201/9781003598152-47
  12. Baturynska I. Application of Machine Learning Techniques to Predict the Mechanical Properties of Polyamide 2200 (PA12) in Additive Manufacturing. Applied Sciences. 2019;9(6):1060. doi:10.3390/app9061060
  13. Isabona, L. L. Ibitome, A. L. Imoize, U. Mamodiya, A. Kumar, M. M. Hassan, et al., "Statistical characterization and modeling of radio frequency signal propagation in mobile broadband cellular next generation wireless networks", Computational Intelligence and Neuroscience, vol. 2023, no. 1, pp. 5236566, 2023 S. K. Sunori,
  14. Cassola S, Duhovic M, Schmidt T, May D. Machine learning for polymer composites process simulation – a review. Composites Part B: Engineering. 2022;246:110208. doi:10.1016/j.compositesb.2022.110208
  15. Mamodiya and N. Tiwari, "Design and implementation of an intelligent single axis automatic solar tracking system", Mater. today Proc., vol. 81, pp. 1148-1151, 2023
  16. Deepak Kumar B N,, Mahesh Dutt K. A Study On Mechanical Properties Of 3d Printed Hybrid Polymer Composites. Journal of Namibian Studies : History Politics Culture. 2023;33:3951-3969. doi:10.59670/btwhrn64
  17. Kishor I. A prototype framework for customizing virtual reality interactions for paralyzed patients integrating physical and rehabilitation modalities. Recent Advances in Sciences, Engineering, Information Technology & Management. 2024:338-344. doi:10.1201/9781003598152-47
  18. Greco PF, Pepi C, Gioffré M. A novel biocomposite material for sustainable constructions: Metakaolin lime mortar and Spanish broom fibers. Journal of Building Engineering. 2024;83:108425. doi:10.1016/j.jobe.2023.108425
  19. Soo XYD, Muiruri JK, Wu WY, Yeo JCC, Wang S, Tomczak N, et al. Bio‐Polyethylene and Polyethylene Biocomposites: An Alternative toward a Sustainable Future. Macromolecular Rapid Communications. 2024;45(14). doi:10.1002/marc.202400064
  20. Zulfiqar A, Shah AUR, Khalil MS, Azad MM, Zulfiqar Y, Naseem MS, et al. Enhancing properties of jute/starch bio-composite material through incorporation of magnesium carbonate hydroxide pentahydrate: A sustainable approach. Materials Chemistry and Physics. 2024;314:128690. doi:10.1016/j.matchemphys.2023.128690
  21. Wang S, Muiruri JK, Soo XYD, Liu S, Thitsartarn W, Tan BH, et al. Bio‐Polypropylene and Polypropylene‐based Biocomposites: Solutions for a Sustainable Future. Chemistry – An Asian Journal. 2022;18(2). doi:10.1002/asia.202200972
  22. Rehman NU, Ullah KS, Sajid M, Ihsanullah I, Waheed A. Preparation of Sustainable Composite Materials from Bio‐Based Domestic and Industrial Waste: Progress, Problems, and Prospects‐ A Review. Advanced Sustainable Systems. 2024;8(8). doi:10.1002/adsu.202300587
  23. McNeill DC, Pal AK, Mohanty AK, Misra M. High biomass filled biodegradable plastic in engineering sustainable composites. Composites Part C: Open Access. 2023;12:100388. doi:10.1016/j.jcomc.2023.100388
  24. Benchouia HE, Boussehel H, Guerira B, Sedira L, Tedeschi C, Becha HE, et al. An experimental evaluation of a hybrid bio-composite based on date palm petiole fibers, expanded polystyrene waste, and gypsum plaster as a sustainable insulating building material. Construction and Building Materials. 2024;422:135735. doi:10.1016/j.conbuildmat.2024.135735
  25. Zhang H, Liao W, Chen G, Ma H. Development and Characterization of Coal-Based Thermoplastic Composite Material for Sustainable Construction. Sustainability. 2023;15(16):12446. doi:10.3390/su151612446
  26. Ferdousi S, Advincula R, Sokolov AP, Choi W, Jiang Y. Investigation of 3D printed lightweight hybrid composites via theoretical modeling and machine learning. Composites Part B: Engineering. 2023;265:110958. doi:10.1016/j.compositesb.2023.110958
  27. Chew AK, Afzal MAF, Chandrasekaran A, Kamps JH, Ramakrishnan V. Designing the next generation of polymers with machine learning and physics-based models. Machine Learning: Science and Technology. 2024;5(4):045031. doi:10.1088/2632-2153/ad88d7
  28. Rabby MM, Das PP, Rahman M, Vadlamudi V, Raihan R. Fast and accurate prediction of cure quality and mechanical performance in fiber‐reinforced polymer composite using dielectric variables and machine learning. Polymer Composites. 2023;45(2):1810-1825. doi:10.1002/pc.27891
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