Investigation on air permeability of finished stretch plain knitted fabrics. I. Predicting air permeability using artificial neural networks
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- 作者：Rania Baghdadi ; Hamza Alibi ; Faten Fayala ; Xianyi Zeng
- 关键词：Air permeability ; Well ; being ; Finishing ; Knit fabrics ; Artificial neural network ; Virtual leave one out
- 刊名：Fibers and Polymers
- 出版年：2016
- 出版时间：December 2016
- 年：2016
- 卷：17
- 期：12
- 页码：2105-2115
- 全文大小：
- 刊物类别：Chemistry and Materials Science
- 刊物主题：Polymer Sciences;
- 出版者：The Korean Fiber Society
- ISSN：1875-0052
- 卷排序：17
文摘
Air permeability is one of the most important utility properties of textile materials as it influences air flow through textile material. Air permeability plays a significant role in well-being due to its influence on physiological comfort. The air permeability of textile materials depends on their porosity. There are a lot of structural properties of textile materials also operating parameters (knitting+finishing) influencing air permeability and there are also statistically significant interactions between the main factors influencing the air permeability of knitted fabrics made from pure yarn cotton (cellulose) and viscose (regenerated cellulose) fibers and plated knitted with elasthane (Lycra) fibers. Two types of artificial neural networks (ANNs) model have been set up before modeling procedure by utilizing multilayer feed forward neural networks, which take into account the generality and the specificity of the product families respectively. A virtual leave one out approach dealing with over fitting phenomenon and allowing the selection of the optimal neural network architecture was used. Moreover this study exhibited that air permeability could be predicted with high accuracy for stretch plain knitted fabrics treated with different finishing processes. Within the framework of the work presented, ANNs were applied to help industry to adjust the operating parameter before the actual manufacturing to reach the desired air permeability and satisfy their consumers.