Jiang Hongxia

Weaving thickness parameters of “8” shape 3D woven enhancing fabric

Purpose – The purpose of this paper is to deduce the thickness property of three‐dimensional (3D) composite produced by 3D woven enhancing fabric based on an academic model.Design/methodology/approach – Thickness of 3D composite is determined by the important weaving parameter – the length of binder yarn. According to the shape of pile formed by binder yarn, curve function of pile is supposed. A rapier loom is modified for the 3D woven enhancing fabrics, and the composite is produced based on the fabric. The thickness of composite is produced and the theories results are validated.Findings – The result of the analysis shows that the curve of pile formed by binder yarn can be expressed as sin function approximately, and there is linear relation between the thickness of composite and the length of pile of binder yarn.Research limitations/implications – The results cannot be provided to study the relationship of thickness based on different technology of composite.Originality/value – The paper provides an ac...

Objective assessment for characterising the flatness of garment sewing stitches

Abstract In this paper, a novel classification method of assessing garment sewing stitch based on amended bi-dimensional empirical mode decomposition (ABEMD) has been introduced. Two parameters that characterise garment sewing stitch, average area and standard deviation, have been defined based on the grey value of pixels. Experimental results showed that when the window size is 512×128 pixels with regard to average area, the threshold can be decided as 6.00, 5.50, 5.30 and 4.00 for five different grades , respectively. Meanwhile, with regard to standard deviation, the threshold can be decided as 48.00, 40.00, 30.00 and 20.00, respectively. It is demonstrated that the parameters are effective in discriminating sewing stitch images in terms of the grades when used as inputs for the ABEMD. The performance of the algorithm on different garment status is significantly reliable.