Machine Learning Approaches to Natural Fiber Composites: A Review of Methodologies and Applications
In recent years, the process of optimizing the design of natural fiber reinforcement in natural fiber composites (NFCs) with distinct properties has been redefined through the application of machine learning (ML). This work elucidates the functions of the types and applications of the ML algorithms...
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| Format: | Article |
| Language: | English |
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North Carolina State University
2025-02-01
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| Series: | BioResources |
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| Online Access: | https://ojs.bioresources.com/index.php/BRJ/article/view/24039 |
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| author | Sivasubramanian Palanisamy Nadir Ayrilmis Kumar Sureshkumar Carlo Santulli Tabrej Khan Harri Junaedi Tamer Ali Sebaey |
| author_facet | Sivasubramanian Palanisamy Nadir Ayrilmis Kumar Sureshkumar Carlo Santulli Tabrej Khan Harri Junaedi Tamer Ali Sebaey |
| author_sort | Sivasubramanian Palanisamy |
| collection | DOAJ |
| description | In recent years, the process of optimizing the design of natural fiber reinforcement in natural fiber composites (NFCs) with distinct properties has been redefined through the application of machine learning (ML). This work elucidates the functions of the types and applications of the ML algorithms and evolutionary computing techniques, with a particular focus on their applicability within the domain of NFCs. Moreover, the solution methodologies and associated databases were employed throughout various stages of the product development journey, from the raw material selection through the final end-use application for the NFCs. The strengths and limitations of the ML in the NFCs industry, together with relevant challenges, such as interpretability of ML models, in materials science was detailed. Finally, future directions and emerging trends in the ML are discussed. |
| format | Article |
| id | doaj-art-4836518067254d4fac1952132a59ea59 |
| institution | Kabale University |
| issn | 1930-2126 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | North Carolina State University |
| record_format | Article |
| series | BioResources |
| spelling | doaj-art-4836518067254d4fac1952132a59ea592025-02-11T00:00:29ZengNorth Carolina State UniversityBioResources1930-21262025-02-01201232123452740Machine Learning Approaches to Natural Fiber Composites: A Review of Methodologies and ApplicationsSivasubramanian Palanisamy0https://orcid.org/0000-0003-1926-4949Nadir Ayrilmis1https://orcid.org/0000-0002-9991-4800Kumar Sureshkumar2Carlo Santulli3https://orcid.org/0000-0002-1686-4271Tabrej Khan4https://orcid.org/0000-0002-8619-1340Harri Junaedi5Tamer Ali Sebaey6https://orcid.org/0000-0001-7696-1973Department of Mechanical Engineering, PTR College of Engineering and Technology, Austinpatti, Madurai, 625008, Tamil Nadu, India; Department of Mechanical Engineering, Chennai Institute of Technology, Sarathy Nagar, Kundrathur, Chennai-600069, Tamilnadu, IndiaDepartment of Wood Mechanics and Technology, Faculty of Forestry, Istanbul University-Cerrahpasa, Istanbul, TurkiyeDept. of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram, Guntur District - 522 302 Andhra Pradesh, IndiaSchool of Science and Technology, Università di Camerino, 62032 Camerino, ItalyDepartment of Engineering Management, College of Engineering, Prince Sultan University, Riyadh- 11586, Saudi ArabiaDepartment of Engineering Management, College of Engineering, Prince Sultan University, Riyadh- 11586, Saudi ArabiaDepartment of Engineering Management, College of Engineering, Prince Sultan University, Riyadh- 11586, Saudi ArabiaIn recent years, the process of optimizing the design of natural fiber reinforcement in natural fiber composites (NFCs) with distinct properties has been redefined through the application of machine learning (ML). This work elucidates the functions of the types and applications of the ML algorithms and evolutionary computing techniques, with a particular focus on their applicability within the domain of NFCs. Moreover, the solution methodologies and associated databases were employed throughout various stages of the product development journey, from the raw material selection through the final end-use application for the NFCs. The strengths and limitations of the ML in the NFCs industry, together with relevant challenges, such as interpretability of ML models, in materials science was detailed. Finally, future directions and emerging trends in the ML are discussed.https://ojs.bioresources.com/index.php/BRJ/article/view/24039machine learningnatural fiber compositesdeep learningstacking sequences |
| spellingShingle | Sivasubramanian Palanisamy Nadir Ayrilmis Kumar Sureshkumar Carlo Santulli Tabrej Khan Harri Junaedi Tamer Ali Sebaey Machine Learning Approaches to Natural Fiber Composites: A Review of Methodologies and Applications BioResources machine learning natural fiber composites deep learning stacking sequences |
| title | Machine Learning Approaches to Natural Fiber Composites: A Review of Methodologies and Applications |
| title_full | Machine Learning Approaches to Natural Fiber Composites: A Review of Methodologies and Applications |
| title_fullStr | Machine Learning Approaches to Natural Fiber Composites: A Review of Methodologies and Applications |
| title_full_unstemmed | Machine Learning Approaches to Natural Fiber Composites: A Review of Methodologies and Applications |
| title_short | Machine Learning Approaches to Natural Fiber Composites: A Review of Methodologies and Applications |
| title_sort | machine learning approaches to natural fiber composites a review of methodologies and applications |
| topic | machine learning natural fiber composites deep learning stacking sequences |
| url | https://ojs.bioresources.com/index.php/BRJ/article/view/24039 |
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