Advances in natural fiber polymer and PLA composites through artificial intelligence and machine learning integration
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 optimiz...
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| Published in | Journal of polymer research Vol. 32; no. 3 |
|---|---|
| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
01.03.2025
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. 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. |
| ArticleNumber | 76 |
| Author | Uddin, Md. Helal Khan, T. M. Yunus Nur-E-Alam, Mohammad Yap, Boon Kar Mulla, Mohammed Huzaifa Shahapurkar, Kiran Soudagar, Manzoore Elahi M. Rajamony, Reji Kumar Abedin, Tarek Manap, Abreeza |
| Author_xml | – sequence: 1 givenname: Md. Helal surname: Uddin fullname: Uddin, Md. Helal email: mdhelaluddinarnob@gmail.com organization: Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional (UNITEN), UNITEN, Department of Mechanical Engineering, College of Engineering, Universiti of Tenaga Nasional (UNITEN), UNITEN – sequence: 2 givenname: Mohammed Huzaifa surname: Mulla fullname: Mulla, Mohammed Huzaifa organization: Division of Research and Development, Lovely Professional University – sequence: 3 givenname: Tarek surname: Abedin fullname: Abedin, Tarek organization: Department of Electrical Engineering, College of Engineering, Universiti of Tenaga Nasional (UNITEN), UNITEN – sequence: 4 givenname: Abreeza surname: Manap fullname: Manap, Abreeza organization: Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional (UNITEN), UNITEN, Department of Mechanical Engineering, College of Engineering, Universiti of Tenaga Nasional (UNITEN), UNITEN – sequence: 5 givenname: Boon Kar surname: Yap fullname: Yap, Boon Kar organization: Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional (UNITEN), UNITEN, Department of Electrical Engineering, College of Engineering, Universiti of Tenaga Nasional (UNITEN), UNITEN – sequence: 6 givenname: Reji Kumar surname: Rajamony fullname: Rajamony, Reji Kumar organization: Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional (UNITEN), UNITEN, Faculty of Engineering and Technology, Parul University – sequence: 7 givenname: Kiran surname: Shahapurkar fullname: Shahapurkar, Kiran organization: Centre of Molecular Medicine and Diagnostics (COMManD), Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University – sequence: 8 givenname: T. M. Yunus surname: Khan fullname: Khan, T. M. Yunus organization: Department of Mechanical Engineering, College of Engineering, King Khalid University – sequence: 9 givenname: Manzoore Elahi M. surname: Soudagar fullname: Soudagar, Manzoore Elahi M. organization: College of Engineering, Lishui University, Chitkara Centre for Research and Development, Chitkara University – sequence: 10 givenname: Mohammad surname: Nur-E-Alam fullname: Nur-E-Alam, Mohammad email: m.nur-e-alam@ecu.edu.au organization: Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional (UNITEN), UNITEN, Centre for Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, School of Science, Edith Cowan University, School of Engineering and Technology, Central Queensland University Australia |
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| Keywords | Artificial Intelligence (AI) Machine Learning (ML) PLA Natural fiber polymer Optimization Decarbonization |
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| Title | Advances in natural fiber polymer and PLA composites through artificial intelligence and machine learning integration |
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