| Issue |
E3S Web Conf.
Volume 428, 2023
2023 Research, Invention, and Innovation Congress (RI2C 2023)
|
|
|---|---|---|
| Article Number | 02013 | |
| Number of page(s) | 7 | |
| Section | Technology for Environment and Sustainable Development | |
| DOI | https://doi.org/10.1051/e3sconf/202342802013 | |
| Published online | 14 September 2023 | |
Machine Learning Techniques for the Design and Optimization of Polymer Composites: A Review
1 Department of Mechanical Engineering, KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore, Tamil Nadu, India.
2 Department of Applied Mechanics, Seenu Atoll School, Hulhu-medhoo, Addu City, Maldives.
3 Natural Composites Research Group Lab, Department of Materials and Production Engineering, The Sirindhorn International ThaiGerman School of Engineering (TGGS), King Mongkut’s University of Technology North Bangkok (KMUTNB), Bangkok, Thailand.
* Corresponding author: indransdesign@gmail.com
Polymer composites are employed in a variety of applications due to their distinctive characteristics. Nevertheless, designing and optimizing these materials can be a lengthy and resourceintensive process for low cost and sustainable materials. Machine learning has the potential to simplify this process by offering predictions of the characteristics of novel composite materials based on their microstructures. This review outlines machine learning techniques and highlights the potential of machine learning to improve the design and optimization of polymer composites. This review also examines the difficulties and restrictions of utilizing machine learning in this context and offers insights into potential future research paths in this field.
Key words: Design / Machine learning / Mechanical properties / Optimization / Physical properties / Polymer composites
© The Authors, published by EDP Sciences, 2023
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