article·2020-03-13

Designing woven velour printed terry fabric for improved colour fastness

Jitendra Pratap Singh, Sanjeev Kumar Ram Sanjeev Kumar Ram

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Abstract

Purpose The purpose of this paper is to investigate the effect of pick density, pile height and pile yarn count (both single- and double-ply yarn) on the colour fastness to crocking, colour fastness to washing, colour fastness to water of woven velour printed terry fabrics. These variables have also been optimized for developing high-quality fabrics. Design/methodology/approach Variables were selected on the basis of past research experience and samples were prepared according to the Box–Behnken design of experiments. The samples were tested for colour fastness to crocking, colour fastness to washing and colour fastness to water by following AATCC 8, AATCC 61, 2A and AATCC 107, respectively. Findings The colour fastness to crocking, washing and water of woven velour printed terry fabrics increases with the decrease in pile height and pick density. The colour fastness properties of the fabric increase with increase in fineness of the pile yarn count. Woven velour printed terry fabric with 16.25 picks per cm, 3.5 mm pile height and 16 Ne pile yarn will show best colour fastness. Woven velour printed terry fabric (plied pile yarn) with 16.25 picks per cm, 3.5 mm pile height and 2/24 Ne pile yarn will show best colour fastness Practical implications Proved a practical approach to control fastness properties of the fabric by changing fabric variables. Originality/value Colour fastness properties of woven velour printed terry fabrics have never been reported. The research work gives the better understanding to develop high quality of fabrics by reducing pile height and pick density. This will also reduce the cost of the fabric.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Textile materials and evaluations · Dyeing and Modifying Textile Fibers · Color perception and design

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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