Browsing by Author "Niles, S. N"

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    PublicationEmbargo
    Analysis of the Dimensional Change of Woven Fabrics from Loom State to Finished State
    (IEEE, 2018-05-30) Kandemulla, K. A. S. M; Maduwantha, A. R. L; Fernando, E. A. S. K; Niles, S. N; Jayawardena, T. S. S
    Dimensional change of a woven fabric is a challenge in woven fabric production. The phenomenon originates from the dimensional instability of the fabric. Shrinkage is a combined result of numerous factors such as relaxation, dyeing, finishing and the effect of machinery. The significance of this problem has been investigated by several researchers, who focused mainly on the geometry of the fabric during the weaving phase (loom stage) only. To investigate the dimensional changes that occur within a particular woven structure, a number of fabric samples were sent through various finishing processes such as, washing, dyeing & finishing, under the laboratory conditions. The changes were measured either in terms of dimensional change or EPI and PPI values. Using the experimental data and theoretical analysis, a mathematical model has been developed and validated. However initially the focus is laid on plain woven fabrics and it is expected to be further extended to the other woven structures as well.
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    PublicationOpen Access
    KERNEL-BASED CLUSTERING APPROACH IN DEVELOPING APPAREL SIZE CHARTS
    (CiteSeerX, 2015-01) Vithanage, C. P; Jayawardena, T. S. S; Thilakaratne, C. D; Niles, S. N
    With the industry revolution, apparel products also become more sophisticated moving from the basic purpose of clothing to aesthetic appeal of the garment embracing the concepts garment fitting and fashion. Garment fitting is a key technical essential for comfortable wearing. In garment fitting, size refers to a set of specified values of body measurements, such that it will provide a means for garments perfectly fit to a person. With the advent of computer software and improved data mining techniques, researchers attempted new advances in formulation of size charts with a better fit. This article suggests a kernel-based clustering approach in developing an effective size chart for the pants of Sri Lankan females. A new kernel based approach “Global Kernel K- means clustering ” was successfully deployed to cluster lower body anthropometric data of Sri Lankan females within the age range of 20-40 years. Through the proposed Kernel- based clustering method can effectively handle highly non-linear data in input space which is a key property of lower body anthropometric data and make it linearly separable in feature space without reduction in dimensions and also mathematically justified. Through this method promising results could be obtained and further clustering method was internally validated with kernel based Dunn’s index. The level of fitness of the developed size chart was also evaluated with the aggregate loss of fit factor. The proposed method has strong
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    Machine learning-based approach for modelling elastic modulus of woven fabrics
    (IEEE, 2020-07-28) Kularatne, S.D.M.W; Ranawaka, R. A. H. S; Fernando, E. A. S. K; Niles, S. N; Jayawardena, T. S. S; Ranaweera, R. K. P. S
    There has been a shift of focus from aesthetic properties to mechanical and functional properties of textiles with the recent developments in technical textiles and wearable technology. Therefore, understanding how various fabric parameters influence the mechanical properties of fabrics is paramount. In applications where compression and stretching of fabrics are important, the elastic modulus is a key fabric property that needed to be controlled precisely. Woven fabrics are capable of providing superior elastic properties, but how various fabric parameters affect elastic modulus is not well understood. In this study, two machine learning techniques were implemented to model the elastic modulus of woven fabrics and were compared with multivariable regressions. The two machine learning techniques used are Artificial Neural Network (ANN) and Random Forest Regression. As input variables; weave factor (numerical representation of weave structure), warp yarn count and pick density were used. Both ANN and Random Forest Regression were able to generate reasonably accurate results with Random Forest Regression been the better of the two methods. Using Random Forest Regression, feature importance of the input variables was obtained, and it proved that the weave structure has a notable impact on the elastic modulus of woven fabrics.