Machine learning in optimization of nonwoven fabric bending rigidity in spunlace production line
Abstract Spunlace nonwoven fabrics have been extensively employed in different applications such as medical, hygienic, and industrial due to their drapeability, soft handle, low cost, and uniform appearance. To manufacture a spunlace nonwoven fabric with desirable properties, production parameters p...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-44571-z |
| _version_ | 1797576906348953600 |
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| author | Mohammad Reza Sadeghi Seyed Mohammad Hosseini Varkiyani Ali Asghar Asgharian Jeddi |
| author_facet | Mohammad Reza Sadeghi Seyed Mohammad Hosseini Varkiyani Ali Asghar Asgharian Jeddi |
| author_sort | Mohammad Reza Sadeghi |
| collection | DOAJ |
| description | Abstract Spunlace nonwoven fabrics have been extensively employed in different applications such as medical, hygienic, and industrial due to their drapeability, soft handle, low cost, and uniform appearance. To manufacture a spunlace nonwoven fabric with desirable properties, production parameters play an important role. Moreover, the relationship between the primary response and input parameter and the relationship between the secondary response and primary responses of spunlace nonwoven fabric were modeled via an artificial neural network (ANN). Furthermore, a multi-objective optimization via genetic algorithm (GA) to find a combination of production parameters to fabricate a sample with the highest bending rigidity and lowest basis weight was carried out. The results of optimization showed that the cost value of the best sample is 0.373. The optimized set of production factors were Young’s modulus of fiber of 0.4195 GPa, the line speed of 53.91 m/min, the average pressure of water jet 42.43 bar, and the feed rate of 219.67 kg/h, which resulted in bending rigidity of 1.43 mN $${\mathrm{cm}}^{2}$$ cm 2 /cm and basis weight of 37.5 gsm. In terms of advancing the textile industry, it is hoped that this work provides insight into engineering the final properties of spunlace nonwoven fabric via the implementation of machine learning. |
| first_indexed | 2024-03-10T22:00:27Z |
| format | Article |
| id | doaj.art-72846bf2551343928a4771d40006f635 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-10T22:00:27Z |
| publishDate | 2023-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-72846bf2551343928a4771d40006f6352023-11-19T12:58:22ZengNature PortfolioScientific Reports2045-23222023-10-0113111710.1038/s41598-023-44571-zMachine learning in optimization of nonwoven fabric bending rigidity in spunlace production lineMohammad Reza Sadeghi0Seyed Mohammad Hosseini Varkiyani1Ali Asghar Asgharian Jeddi2Department of Textile Engineering, Amirkabir University of TechnologyDepartment of Textile Engineering, Amirkabir University of TechnologyDepartment of Textile Engineering, Amirkabir University of TechnologyAbstract Spunlace nonwoven fabrics have been extensively employed in different applications such as medical, hygienic, and industrial due to their drapeability, soft handle, low cost, and uniform appearance. To manufacture a spunlace nonwoven fabric with desirable properties, production parameters play an important role. Moreover, the relationship between the primary response and input parameter and the relationship between the secondary response and primary responses of spunlace nonwoven fabric were modeled via an artificial neural network (ANN). Furthermore, a multi-objective optimization via genetic algorithm (GA) to find a combination of production parameters to fabricate a sample with the highest bending rigidity and lowest basis weight was carried out. The results of optimization showed that the cost value of the best sample is 0.373. The optimized set of production factors were Young’s modulus of fiber of 0.4195 GPa, the line speed of 53.91 m/min, the average pressure of water jet 42.43 bar, and the feed rate of 219.67 kg/h, which resulted in bending rigidity of 1.43 mN $${\mathrm{cm}}^{2}$$ cm 2 /cm and basis weight of 37.5 gsm. In terms of advancing the textile industry, it is hoped that this work provides insight into engineering the final properties of spunlace nonwoven fabric via the implementation of machine learning.https://doi.org/10.1038/s41598-023-44571-z |
| spellingShingle | Mohammad Reza Sadeghi Seyed Mohammad Hosseini Varkiyani Ali Asghar Asgharian Jeddi Machine learning in optimization of nonwoven fabric bending rigidity in spunlace production line Scientific Reports |
| title | Machine learning in optimization of nonwoven fabric bending rigidity in spunlace production line |
| title_full | Machine learning in optimization of nonwoven fabric bending rigidity in spunlace production line |
| title_fullStr | Machine learning in optimization of nonwoven fabric bending rigidity in spunlace production line |
| title_full_unstemmed | Machine learning in optimization of nonwoven fabric bending rigidity in spunlace production line |
| title_short | Machine learning in optimization of nonwoven fabric bending rigidity in spunlace production line |
| title_sort | machine learning in optimization of nonwoven fabric bending rigidity in spunlace production line |
| url | https://doi.org/10.1038/s41598-023-44571-z |
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