By J Hu
Desktop know-how has remodeled textiles from their layout via to their manufacture. This publication displays the numerous advances which were made in machine applied sciences, masking quite a lot of themes from modeling to digital textiles and clothing. half 1 presents a evaluation of other desktop established applied sciences compatible for cloth fabrics. Chapters contain desktop expertise for yarn constitution, textile defects and garment constitution. half 2 discusses modeling and simulation rules of fibers and textiles. Examples of themes contain the modeling of yarn and clothes. half three concludes with a evaluation of desktop dependent applied sciences particular to clothing, subject matters diversity from 3D physique scanning to digital textiles.
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Additional resources for Computer Technology for Textiles and Apparel (Woodhead Publishing Series in Textiles)
For many years, yarn irregularity has been measured by the capacitance evenness tester using two parallel capacitive sensors. The capacitance based method is accurate and stable in yarn mass measurement and has been well accepted in the textile industry for decades. Nevertheless this method can only give a rough description of yarn irregularity in diameter. Optical measurement alternatively provides a more accurate method in determining the yarn diameter and its variation by using optical sensors.
Watanabe A, Kurosaki S N, Konda F and Nishimura Y (1992b), ‘Analysis of blend irregularity in yarns using image-processing. II. Applying the system to actual blended yarns’, Text Res J, 62, 729–735. Watanabe A, Konda F and Kurosaki S N (1995), ‘Analysis of blend irregularity in yarns using image processing. III. Evaluation of blend irregularity by line sense and its application to actual blended yarns’, Text Res J, 65, 392–399. Xu B, Dong B and Chen Y (2007), ‘Neural network technique for fiber image recognition’, J Indust Text, 36, 329–336.
25 − ( z − μ l )2 e 2σ l2 , l = 1, 2, . . 5 Yarn density profile (pixels) The normalized Gaussian function is chosen because its shape can well match the morphological property of a yarn snarl. 14 shows the decomposition results of the yarn density profile in terms of Gaussian functions. There are a total of 15 Gaussian functions used for the decomposition, therefore the number of yarn snarl turns in Fig. 5 turns. With these Gaussian functions, it is also feasible to reconstruct a profile by adding these Gaussian functions for a comparison with the original yarn density profile, as shown in Fig.