This paper presents a comparative study of three modeling methodologies for predicting the breaking elongation of ring spun cotton yarns. Constituent cotton fiber properties and yarn count are used as inputs to these models. The predictive powers of the three different models—mathematical, statistical, and artificial neural network—are estimated and com pared. The relative importance of various cotton fiber properties measured by a high volume instrument is also investigated using the artificial neural network model.

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Ranking and selection of textile fabrics for a particular end-use requirement is a complex task. In this article, an attempt has been made to develop a simple index of handloom fabric quality, which can be used for selecting fabrics for a specified end use. The Analytic Hierarchy Process (AHP) and Multiplicative Analytic Hierarchy Process (MAHP) of multi-criteria decision making (MCDM) have been used for ranking 25 handloom cotton fabrics in terms of their overall quality value considering their applicability as summer clothing materials. The rank correlation between the rankings elicited from two MCDM methods was found to be 0.926 which implies that the rankings given by AHP and MAHP are in high degree of agreement with each other and any of the two methods can be chosen for ranking of fabrics.

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. ABSTRACTThis paper presents a comprehensive review of soft computing applications in the domain of fabrics and clothing. In the last two decades, soft computing techniques, such as artificial neural network, fuzzy logic and genetic algorithm, have been used abundantly for fabrics and clothing modelling, manufacturing, quality control and marketing. This review is aimed at presenting a compendium of research work done so far on the applications of soft computing techniques in fabrics and clothing science and engineering. In the beginning of the paper, a brief introduction of soft computing techniques is provided. Then, the applications of soft computing methods in fabric property modelling (tensile, bending, shear, drape, handle, comfort, thickness and compression, air permeability, porosity, etc.) are provided. In the subsequent sections, soft computing applications for fabric defect identification in static and dynamic conditions, fabric classification, fabric engineering, machine control and marketing, are discussed. The scope of future applications is included in the concluding section.