Artificial Intelligence-Assisted Multi-Objective Optimization of Agricultural Biomass-Reinforced Polymer Composites

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Year : 2026 | Volume : 14 | 04 | Page :
By

Tejaswini R. Murgod,

Nagarathna C. R,

Nandini G,

Pallavi Hallappanavar Basavaraja,

Keerti Patil,

  1. Professor, Department of Artificial Intelligence and Machine Learning (AIML), BNM Institute of Technology, Bengaluru, Karnataka, India
  2. Associate Professor, Department of Machine Learning, BMS College of Engineering, Bengaluru, Karnataka, India
  3. Associate Professor, Department of Computer Science and Engineering, B.N.M. Institute of Technology, Banashankari 2nd Stage, Bengaluru, Karnataka, India
  4. Associate Professor, Department of Information Science and Engineering (IS&E), Dayananda Sagar Academy of Technology and Management, Bengaluru, Karnataka, India
  5. Assistant Professor, Department of Computer Science & Engineering, Dayananda Sagar College of Engineering, Shavige Malleshwara Hills, 91st Main Road, 1st Stage, Kumaraswamy Layout, Bengaluru, Karnataka, India

Abstract

Agricultural biomass can reduce the environmental burden of polymer composites, yet its heterogeneous structure creates competing effects on strength, moisture resistance, density, and process ability. This study developed an artificial intelligence-assisted framework for balanced composite formulation. Experimental data of agricultural biomass reinforced polymer composites were gathered, harmonized and validated using leakage controlled validation. The mechanical and physical properties were predicted by artificial neural networks and conventional regression models. Explainable analysis gave the dominant material variables and NSGA-II gave the Pareto optimal formulations within the constraints that were supported by experiments. The neural model was able to achieve a mean prediction error of 46.3% less than linear regression. The density decreased by 6.36% while the compressive strength increased by 30.8% with an increase in biomass loading from 10 to 40 wt.%, but the flexural strength decreased by 28.1% and the water absorption increased by 113.6%. Multi-objective analysis revealed that a formulation of 20 wt.% biomass was the best compromise, while 40 wt.% was more suitable for lightweight, impact resistant and compression dominated applications. The proposed workflow transforms experimental composite data to design decisions that can be interpreted and does not involve single property optimization. It provides a repeatable pathway to choosing biomass–polymer formulations that are both resource efficient and have the same engineering performance and material reliability.

Keywords: Agricultural biomass, Polymer composites, Artificial intelligence, Multi-objective optimization, Explainable machine learning

How to cite this article: Tejaswini R. Murgod, Nagarathna C. R, Nandini G, Pallavi Hallappanavar Basavaraja, Keerti Patil. Artificial Intelligence-Assisted Multi-Objective Optimization of Agricultural Biomass-Reinforced Polymer Composites. Journal of Polymer & Composites. 2026; 14(04):-.
How to cite this URL: Tejaswini R. Murgod, Nagarathna C. R, Nandini G, Pallavi Hallappanavar Basavaraja, Keerti Patil. Artificial Intelligence-Assisted Multi-Objective Optimization of Agricultural Biomass-Reinforced Polymer Composites. Journal of Polymer & Composites. 2026; 14(04):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=251951

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Ahead of Print Subscription Original Research
Volume 14
04
Received 21/07/2026
Accepted 05/08/2026
Published 08/08/2026
Publication Time 18 Days

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