Development of Prediction System for Shade Change Variations in Dyed Cotton Fabric After Application of Water Repellent Finishes
Textile finishing is the last stage to improve fabric aesthetic characteristic and impart functional properties, but at the same time it can produce some undesirable effects like shade change and variation in mechanical properties of fabric. These shade variations are undesirable and create major lo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-04-01
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| Series: | Journal of Natural Fibers |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/15440478.2022.2154302 |
| _version_ | 1797674716327051264 |
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| author | Farida Irshad Munir Ashraf Assad Farooq Muhammad Azeem Ashraf Nayab Khan |
| author_facet | Farida Irshad Munir Ashraf Assad Farooq Muhammad Azeem Ashraf Nayab Khan |
| author_sort | Farida Irshad |
| collection | DOAJ |
| description | Textile finishing is the last stage to improve fabric aesthetic characteristic and impart functional properties, but at the same time it can produce some undesirable effects like shade change and variation in mechanical properties of fabric. These shade variations are undesirable and create major losses for the textile industry. These losses are related to rework and reprocessing of dyed fabric after finishing. To cope this issue, dyers are making decision on trial and error bases, therefore, this work has been conducted to quantify the shade change value. In this research work, an artificial intelligence-based system is developed to foresee the behavior of color before finishing. Color, shade percentage, finish type, finish concentration, and 31 reflectance values in the visible range 400–700 nm were selected as input for the training of artificial neural networks. The five networks were trained individually for the Δ color coordinates (△L, △a, △b, △C and △h). The networks were tested and cross-validated with 85% accuracy. The developed models were executed for the predictions of △L, △a, △b, △C, and △h with mean absolute errors 0.0765, 0.0869, 0.1528, 0.0829 and 0.1626, respectively. Mean absolute error values are showing a close correlation between actual and predicted values. |
| first_indexed | 2024-03-11T22:02:58Z |
| format | Article |
| id | doaj.art-b0e56cf0f15b4df6b53f8ddbb1e6a0f2 |
| institution | Directory Open Access Journal |
| issn | 1544-0478 1544-046X |
| language | English |
| last_indexed | 2024-03-11T22:02:58Z |
| publishDate | 2023-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Natural Fibers |
| spelling | doaj.art-b0e56cf0f15b4df6b53f8ddbb1e6a0f22023-09-25T10:28:58ZengTaylor & Francis GroupJournal of Natural Fibers1544-04781544-046X2023-04-0120110.1080/15440478.2022.21543022154302Development of Prediction System for Shade Change Variations in Dyed Cotton Fabric After Application of Water Repellent FinishesFarida Irshad0Munir Ashraf1Assad Farooq2Muhammad Azeem Ashraf3Nayab Khan4University of AgricultureNational Textile UniversityUniversity of AgricultureUniversity of AgricultureUniversity of AgricultureTextile finishing is the last stage to improve fabric aesthetic characteristic and impart functional properties, but at the same time it can produce some undesirable effects like shade change and variation in mechanical properties of fabric. These shade variations are undesirable and create major losses for the textile industry. These losses are related to rework and reprocessing of dyed fabric after finishing. To cope this issue, dyers are making decision on trial and error bases, therefore, this work has been conducted to quantify the shade change value. In this research work, an artificial intelligence-based system is developed to foresee the behavior of color before finishing. Color, shade percentage, finish type, finish concentration, and 31 reflectance values in the visible range 400–700 nm were selected as input for the training of artificial neural networks. The five networks were trained individually for the Δ color coordinates (△L, △a, △b, △C and △h). The networks were tested and cross-validated with 85% accuracy. The developed models were executed for the predictions of △L, △a, △b, △C, and △h with mean absolute errors 0.0765, 0.0869, 0.1528, 0.0829 and 0.1626, respectively. Mean absolute error values are showing a close correlation between actual and predicted values.http://dx.doi.org/10.1080/15440478.2022.2154302artificial neural networkswater repellent finishingshade changecolor matchingcolor coordinates |
| spellingShingle | Farida Irshad Munir Ashraf Assad Farooq Muhammad Azeem Ashraf Nayab Khan Development of Prediction System for Shade Change Variations in Dyed Cotton Fabric After Application of Water Repellent Finishes Journal of Natural Fibers artificial neural networks water repellent finishing shade change color matching color coordinates |
| title | Development of Prediction System for Shade Change Variations in Dyed Cotton Fabric After Application of Water Repellent Finishes |
| title_full | Development of Prediction System for Shade Change Variations in Dyed Cotton Fabric After Application of Water Repellent Finishes |
| title_fullStr | Development of Prediction System for Shade Change Variations in Dyed Cotton Fabric After Application of Water Repellent Finishes |
| title_full_unstemmed | Development of Prediction System for Shade Change Variations in Dyed Cotton Fabric After Application of Water Repellent Finishes |
| title_short | Development of Prediction System for Shade Change Variations in Dyed Cotton Fabric After Application of Water Repellent Finishes |
| title_sort | development of prediction system for shade change variations in dyed cotton fabric after application of water repellent finishes |
| topic | artificial neural networks water repellent finishing shade change color matching color coordinates |
| url | http://dx.doi.org/10.1080/15440478.2022.2154302 |
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