Bridging Nanomanufacturing and Artificial Intelligence—A Comprehensive Review
Nanomanufacturing and digital manufacturing (DM) are defining the forefront of the fourth industrial revolution—Industry 4.0—as enabling technologies for the processing of materials spanning several length scales. This review delineates the evolution of nanomaterials and nanomanufacturing in the dig...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-04-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/17/7/1621 |
| _version_ | 1797212299225726976 |
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| author | Mutha Nandipati Olukayode Fatoki Salil Desai |
| author_facet | Mutha Nandipati Olukayode Fatoki Salil Desai |
| author_sort | Mutha Nandipati |
| collection | DOAJ |
| description | Nanomanufacturing and digital manufacturing (DM) are defining the forefront of the fourth industrial revolution—Industry 4.0—as enabling technologies for the processing of materials spanning several length scales. This review delineates the evolution of nanomaterials and nanomanufacturing in the digital age for applications in medicine, robotics, sensory technology, semiconductors, and consumer electronics. The incorporation of artificial intelligence (AI) tools to explore nanomaterial synthesis, optimize nanomanufacturing processes, and aid high-fidelity nanoscale characterization is discussed. This paper elaborates on different machine-learning and deep-learning algorithms for analyzing nanoscale images, designing nanomaterials, and nano quality assurance. The challenges associated with the application of machine- and deep-learning models to achieve robust and accurate predictions are outlined. The prospects of incorporating sophisticated AI algorithms such as reinforced learning, explainable artificial intelligence (XAI), big data analytics for material synthesis, manufacturing process innovation, and nanosystem integration are discussed. |
| first_indexed | 2024-04-24T10:40:10Z |
| format | Article |
| id | doaj.art-711a60ab0f3d4ce2ae69657f590f7e40 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-04-24T10:40:10Z |
| publishDate | 2024-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-711a60ab0f3d4ce2ae69657f590f7e402024-04-12T13:22:09ZengMDPI AGMaterials1996-19442024-04-01177162110.3390/ma17071621Bridging Nanomanufacturing and Artificial Intelligence—A Comprehensive ReviewMutha Nandipati0Olukayode Fatoki1Salil Desai2Department of Industrial and Systems Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USADepartment of Industrial and Systems Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USADepartment of Industrial and Systems Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USANanomanufacturing and digital manufacturing (DM) are defining the forefront of the fourth industrial revolution—Industry 4.0—as enabling technologies for the processing of materials spanning several length scales. This review delineates the evolution of nanomaterials and nanomanufacturing in the digital age for applications in medicine, robotics, sensory technology, semiconductors, and consumer electronics. The incorporation of artificial intelligence (AI) tools to explore nanomaterial synthesis, optimize nanomanufacturing processes, and aid high-fidelity nanoscale characterization is discussed. This paper elaborates on different machine-learning and deep-learning algorithms for analyzing nanoscale images, designing nanomaterials, and nano quality assurance. The challenges associated with the application of machine- and deep-learning models to achieve robust and accurate predictions are outlined. The prospects of incorporating sophisticated AI algorithms such as reinforced learning, explainable artificial intelligence (XAI), big data analytics for material synthesis, manufacturing process innovation, and nanosystem integration are discussed.https://www.mdpi.com/1996-1944/17/7/1621artificial intelligencedigital manufacturingnanocharacterizationnanomaterialsnanomanufacturing |
| spellingShingle | Mutha Nandipati Olukayode Fatoki Salil Desai Bridging Nanomanufacturing and Artificial Intelligence—A Comprehensive Review Materials artificial intelligence digital manufacturing nanocharacterization nanomaterials nanomanufacturing |
| title | Bridging Nanomanufacturing and Artificial Intelligence—A Comprehensive Review |
| title_full | Bridging Nanomanufacturing and Artificial Intelligence—A Comprehensive Review |
| title_fullStr | Bridging Nanomanufacturing and Artificial Intelligence—A Comprehensive Review |
| title_full_unstemmed | Bridging Nanomanufacturing and Artificial Intelligence—A Comprehensive Review |
| title_short | Bridging Nanomanufacturing and Artificial Intelligence—A Comprehensive Review |
| title_sort | bridging nanomanufacturing and artificial intelligence a comprehensive review |
| topic | artificial intelligence digital manufacturing nanocharacterization nanomaterials nanomanufacturing |
| url | https://www.mdpi.com/1996-1944/17/7/1621 |
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