Modelling and Predicting the Breaking Strength and Mass Irregularity of Cotton Rotor-Spun Yarns Containing Cotton Fiber Recovered from Ginning Process by Using Artificial Neural Network Algorithm
One of the main methods to reduce the production costs is waste recycling which is the most important challenge for the future. Cotton wastes collected from ginning process have desirable properties which could be used during spinning process. The purpose of this study was to develop predictive mode...
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| Format: | Article |
| Language: | English |
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Wiley
2011-01-01
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| Series: | Modelling and Simulation in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2011/591905 |
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| author | Mohsen Shanbeh Hossein Hasani Somayeh Akhavan Tabatabaei |
| author_facet | Mohsen Shanbeh Hossein Hasani Somayeh Akhavan Tabatabaei |
| author_sort | Mohsen Shanbeh |
| collection | DOAJ |
| description | One of the main methods to reduce the production costs is waste recycling which is the most important challenge for the future. Cotton wastes collected from ginning process have desirable properties which could be used during spinning process. The purpose of this study was to develop predictive models of breaking strength and mass irregularity (CV𝑚%) of cotton waste rotor-spun yarns containing cotton waste collected from ginning process by using the artificial neural network trained with backpropagation algorithm. Artificial neural network models have been developed based on rotor diameter, rotor speed, navel type, opener roller speed, ginning waste proportion and yarn linear density as input parameters. The parameters of artificial neural network model, namely, learning, and momentum rate, number of hidden layers and number of hidden processing elements (neurons) were optimized to get the best predictive models. The findings showed that the breaking strength and mass irregularity of rotor spun yarns could be predicted satisfactorily by artificial neural network. The maximum error in predicting the breaking strength and mass irregularity of testing data was 8.34% and 6.65%, respectively. |
| format | Article |
| id | doaj-art-346b98a8f8ae4c0282e6cbc5b902aa9a |
| institution | OA Journals |
| issn | 1687-5591 1687-5605 |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Modelling and Simulation in Engineering |
| spelling | doaj-art-346b98a8f8ae4c0282e6cbc5b902aa9a2025-08-20T02:24:04ZengWileyModelling and Simulation in Engineering1687-55911687-56052011-01-01201110.1155/2011/591905591905Modelling and Predicting the Breaking Strength and Mass Irregularity of Cotton Rotor-Spun Yarns Containing Cotton Fiber Recovered from Ginning Process by Using Artificial Neural Network AlgorithmMohsen Shanbeh0Hossein Hasani1Somayeh Akhavan Tabatabaei2Textile Engineering Department, Isfahan University of Technology, Isfahan 84156-83111, IranTextile Engineering Department, Isfahan University of Technology, Isfahan 84156-83111, IranTextile Engineering Department, Isfahan University of Technology, Isfahan 84156-83111, IranOne of the main methods to reduce the production costs is waste recycling which is the most important challenge for the future. Cotton wastes collected from ginning process have desirable properties which could be used during spinning process. The purpose of this study was to develop predictive models of breaking strength and mass irregularity (CV𝑚%) of cotton waste rotor-spun yarns containing cotton waste collected from ginning process by using the artificial neural network trained with backpropagation algorithm. Artificial neural network models have been developed based on rotor diameter, rotor speed, navel type, opener roller speed, ginning waste proportion and yarn linear density as input parameters. The parameters of artificial neural network model, namely, learning, and momentum rate, number of hidden layers and number of hidden processing elements (neurons) were optimized to get the best predictive models. The findings showed that the breaking strength and mass irregularity of rotor spun yarns could be predicted satisfactorily by artificial neural network. The maximum error in predicting the breaking strength and mass irregularity of testing data was 8.34% and 6.65%, respectively.http://dx.doi.org/10.1155/2011/591905 |
| spellingShingle | Mohsen Shanbeh Hossein Hasani Somayeh Akhavan Tabatabaei Modelling and Predicting the Breaking Strength and Mass Irregularity of Cotton Rotor-Spun Yarns Containing Cotton Fiber Recovered from Ginning Process by Using Artificial Neural Network Algorithm Modelling and Simulation in Engineering |
| title | Modelling and Predicting the Breaking Strength and Mass Irregularity of Cotton Rotor-Spun Yarns Containing Cotton Fiber Recovered from Ginning Process by Using Artificial Neural Network Algorithm |
| title_full | Modelling and Predicting the Breaking Strength and Mass Irregularity of Cotton Rotor-Spun Yarns Containing Cotton Fiber Recovered from Ginning Process by Using Artificial Neural Network Algorithm |
| title_fullStr | Modelling and Predicting the Breaking Strength and Mass Irregularity of Cotton Rotor-Spun Yarns Containing Cotton Fiber Recovered from Ginning Process by Using Artificial Neural Network Algorithm |
| title_full_unstemmed | Modelling and Predicting the Breaking Strength and Mass Irregularity of Cotton Rotor-Spun Yarns Containing Cotton Fiber Recovered from Ginning Process by Using Artificial Neural Network Algorithm |
| title_short | Modelling and Predicting the Breaking Strength and Mass Irregularity of Cotton Rotor-Spun Yarns Containing Cotton Fiber Recovered from Ginning Process by Using Artificial Neural Network Algorithm |
| title_sort | modelling and predicting the breaking strength and mass irregularity of cotton rotor spun yarns containing cotton fiber recovered from ginning process by using artificial neural network algorithm |
| url | http://dx.doi.org/10.1155/2011/591905 |
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