Contribution on Modeling of Bagged Denim Fabric Behavior After Mixed Washing Using PCA and Regression Techniques
This study evaluates the effect of mixed washing treatment on the bagging behavior of denim fabrics. Six types of denim fabrics were washed under different conditions. Then, the bagging test was applied. Furthermore, the most influential factors of this treatment on the bagging ability of denim fabr...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Journal of Natural Fibers |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/15440478.2024.2315082 |
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| author | Abir Ben Fraj Boubaker Jaouachi |
| author_facet | Abir Ben Fraj Boubaker Jaouachi |
| author_sort | Abir Ben Fraj |
| collection | DOAJ |
| description | This study evaluates the effect of mixed washing treatment on the bagging behavior of denim fabrics. Six types of denim fabrics were washed under different conditions. Then, the bagging test was applied. Furthermore, the most influential factors of this treatment on the bagging ability of denim fabric were discussed using the main effect plots. This method shows that the quantity of pumice stone, the quantity of enzyme and the fabric are the most influencing factors. The increase in the amount of enzyme or pumice stone increases the occurrence of the bagging problem. Since the fabric is the dominant factor for all bagging properties, the characterization with Kawabata instruments of treated and untreated fabrics was performed. Then, we used the principal component analysis method to select the most important parameters. A reduction of 52.78% in parameters was achieved. Finally, the linear regression models were obtained to predict the bagging properties. The regression coefficients (R2) range between 96.85% and 99.43% and the adjusted R2 is between 92.34% and 98.13%, which confirms the efficiency of our models. These models are crucial for the manufacturers of washing denim since they could help the industrials to predict denim fabric’s bagging behavior after mixed washing. |
| format | Article |
| id | doaj-art-ee4bed9274b54449b2d0a4648a76effe |
| institution | Kabale University |
| issn | 1544-0478 1544-046X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Natural Fibers |
| spelling | doaj-art-ee4bed9274b54449b2d0a4648a76effe2024-12-09T14:41:33ZengTaylor & Francis GroupJournal of Natural Fibers1544-04781544-046X2024-12-0121110.1080/15440478.2024.2315082Contribution on Modeling of Bagged Denim Fabric Behavior After Mixed Washing Using PCA and Regression TechniquesAbir Ben Fraj0Boubaker Jaouachi1Textile Engineering Laboratory of the Higher Institute of Technological Studies of KsarHellal, University of Monastir-Tunisia, Monastir, TunisiaTextile Engineering Laboratory of the Higher Institute of Technological Studies of KsarHellal, University of Monastir-Tunisia, Monastir, TunisiaThis study evaluates the effect of mixed washing treatment on the bagging behavior of denim fabrics. Six types of denim fabrics were washed under different conditions. Then, the bagging test was applied. Furthermore, the most influential factors of this treatment on the bagging ability of denim fabric were discussed using the main effect plots. This method shows that the quantity of pumice stone, the quantity of enzyme and the fabric are the most influencing factors. The increase in the amount of enzyme or pumice stone increases the occurrence of the bagging problem. Since the fabric is the dominant factor for all bagging properties, the characterization with Kawabata instruments of treated and untreated fabrics was performed. Then, we used the principal component analysis method to select the most important parameters. A reduction of 52.78% in parameters was achieved. Finally, the linear regression models were obtained to predict the bagging properties. The regression coefficients (R2) range between 96.85% and 99.43% and the adjusted R2 is between 92.34% and 98.13%, which confirms the efficiency of our models. These models are crucial for the manufacturers of washing denim since they could help the industrials to predict denim fabric’s bagging behavior after mixed washing.https://www.tandfonline.com/doi/10.1080/15440478.2024.2315082Bagging problemmixed washingdenim fabricmain effect plotsPCA methodregression model |
| spellingShingle | Abir Ben Fraj Boubaker Jaouachi Contribution on Modeling of Bagged Denim Fabric Behavior After Mixed Washing Using PCA and Regression Techniques Journal of Natural Fibers Bagging problem mixed washing denim fabric main effect plots PCA method regression model |
| title | Contribution on Modeling of Bagged Denim Fabric Behavior After Mixed Washing Using PCA and Regression Techniques |
| title_full | Contribution on Modeling of Bagged Denim Fabric Behavior After Mixed Washing Using PCA and Regression Techniques |
| title_fullStr | Contribution on Modeling of Bagged Denim Fabric Behavior After Mixed Washing Using PCA and Regression Techniques |
| title_full_unstemmed | Contribution on Modeling of Bagged Denim Fabric Behavior After Mixed Washing Using PCA and Regression Techniques |
| title_short | Contribution on Modeling of Bagged Denim Fabric Behavior After Mixed Washing Using PCA and Regression Techniques |
| title_sort | contribution on modeling of bagged denim fabric behavior after mixed washing using pca and regression techniques |
| topic | Bagging problem mixed washing denim fabric main effect plots PCA method regression model |
| url | https://www.tandfonline.com/doi/10.1080/15440478.2024.2315082 |
| work_keys_str_mv | AT abirbenfraj contributiononmodelingofbaggeddenimfabricbehavioraftermixedwashingusingpcaandregressiontechniques AT boubakerjaouachi contributiononmodelingofbaggeddenimfabricbehavioraftermixedwashingusingpcaandregressiontechniques |