Advances in natural fiber polymer and PLA composites through artificial intelligence and machine learning integration

• Published in: Journal of polymer research Vol. 32; no. 3
• Main Authors: Uddin, Md. Helal, Mulla, Mohammed Huzaifa, Abedin, Tarek, Manap, Abreeza, Yap, Boon Kar, Rajamony, Reji Kumar, Shahapurkar, Kiran, Khan, T. M. Yunus, Soudagar, Manzoore Elahi M., Nur-E-Alam, Mohammad
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.03.2025; Springer Nature B.V
• Subjects: Artificial intelligence; Automotive parts; Biomedical materials; Characterization and Evaluation of Materials; Chemistry; Chemistry and Materials Science; Composite materials; Industrial Chemistry/Chemical Engineering; Machine learning; Polylactic acid; Polymer Sciences; Polymers; Review Paper; Artificial Intelligence (AI); Machine Learning (ML); PLA; Natural fiber polymer; Optimization; Decarbonization
• ISSN: 1022-9760; 1572-8935
• DOI: 10.1007/s10965-025-04282-7

Natural Fibre Polymer (NFP) and Polylactic Acid (PLA) composites have received a lot of interest in a variety of sectors because they are environmentally friendly, renewable, and sustainable. Over the last decade, researchers have investigated the aspects of NFP/PLA composite development and optimization for a wide range of applications, including packaging materials, automotive components, construction materials, textile and apparel, biomedical devices, agricultural and horticultural applications, electronics, and consumer electronics. Furthermore, using Artificial Intelligence (AI) and Machine Learning (ML) methodologies has increased these polymer materials and associated technologies in their search for new potential ways to further progress in NFP and PLA composites. The purpose of this review paper is to present a complete overview of AI and machine learning applications in the synthesis and development of NFP/PLA composite materials. The subject matter includes the following research areas: material characterization, manufacturing, property prediction, durability assessment, sustainability analysis, and future perspectives, which demonstrate the potential and challenges of AI/ML in advancing NFP/PLA composite materials and technologies.