Optimisation of a Textile Process Using Taguchi Method

Abstract

Taguchi Method is explained in brief and one of the textile processes, called spinning process, is optimised by using Taguchi method for manufacturing optimum packing density of different technologies yarns. The effect of process variables like: lap hank, card draft, draft/doublings and drafts at speed frame, ring frame, rotor and air-jet spinning machine on packing density parameters of respective technology yarn was analysed. The effect of noise variables was also accounted for. The trends of change in packing density with process variables are opposite to those of yarn diameter and helix angle of ring, rotor and air-jet yarns studied. The packing density is found to be the highest in air-jet yarn and the lowest in rotor yarn. Increase in draft in air-jet spinner and decrease in rotor spinner increase packing density of the respective yarns. The change in noise variables does affect the packing density parameters of yarns. Result analysis using Taguchi Method was according to the perception of some of the previous researcher on the subject. Hence, Taguchi method can be also used to optimise a textile process, where the product quality is highly variable and dependent on the combination of number of processes as well as on machine parameters.

Micro/nano-encapsulation of a phase change material by coaxial electrospray method

“…The S/N ratio is expressed in decibels (dB), and its higher value denotes the optimum set of process variables for reading the plot. Calculation of formulas of the S/N ratio is given as follows [34,35].…”

Dyeing process parameters optimisation and colour strength prediction for viscose/lycra blended knitted fabrics using Taguchi method

“…The main effects analysis is used to determine the optimal combination of processing parameters at the specific level with highest mean response while ANOVA is engaged to estimate the error variance and find out the significance of the particular parameters (Fie et al, 2013;Kumar & Ishtiaque, 2009;Kuo et al, 2008;Zeydan, 2008). Taguchi design of experiments is easy to adopt and apply for users with limited knowledge of statistics (Krishankant et al, 2012).…”

“…whilst other designs provide collective effect of variable in terms of equations or three-dimensional curves or contour diagrams, which are frequently not easy to understand and interpret (Kumar & Ishtiaque, 2009). Unlike, conventional trial-and-error approach, full factor experimental design as well as ANN and ANFIS methods, Taguchi method no need enormous amount of experimental data for parameters optimisation.…”

“…In spite of all superior feature of Taguchi method than other approaches, there are still few drawbacks in this method. Major problem in the Taguchi technique is that it cannot always find out the interactions effects like some of the other design of experiment techniques (Kumar & Ishtiaque, 2009). Additionally, since, Taguchi mathematical models are developed on the basis of fundamental theories of basic sciences and developed on the foundation of assumption or simplification; hence, the prediction accuracy of mathematical model is not very encouraging (Majumdar & Ghosh, 2008).…”