Modelling the woven fabric strength using artificial neural network and Taguchi methodologies

Abstract

Purpose -Jacquard woven fabrics are widely used in various sections of upholstery industry, where mattress cover is one of them. Strength of jacquard woven mattress fabric depends on several factors. The objective of this study is to model the multi-linear relationship between fibre, yarn and fabric parameters on the strength of fabric using artificial neural network (ANN) and Taguchi design of experiment (TDOE) methodologies. Design/methodology/approach -TDOE was applied to determine the optimum design values and the contribution of each parameter. Robustness (performance) of models is measured by root mean squared error (RMSE). These tools will enable the user to predict the fabric strength from number of given inputs. It also provides the knowledge related to the contribution of fibre, yarn and fabric parameters on fabric strength. Fabrics tested in this study made from different fibre types and max/min level for several fabric and yarn-related parameters. The models generated with TDOE and ANN methodologies were compared with the actual experimental data. Findings -It was found that ANN model gives better approximation with the minimum RMSE.Research limitations/implications -The data taken from factory are related with jacquard woven fabric.Practical implications -This study has many practical implications that brings up a general approach for textile industry. During manufacturing, waste or scrap ratio can be reduced and production planning become more efficient. Originality/value -Firstly, before starting manufacturing in factory, we can easily predict the strength of woven fabric using the defined factors. This makes the model usable at the planning stage of the fabric. Secondly, the contribution of factors affecting fabric strength was determined. The ANN model generated in this study helps the engineers of planning department at the company easy to plan the manufacturing of fabric with a good estimation of fabric strength before the production order.

Prediction of Fabric Tensile Strength By Modelling the Woven Fabric

“…Therefore, the solution of the fabric strength prediction problem could be performed by employing the empirical and computational models such as artificial neural network (ANN) or classical regression analysis (Majumdar et al, 2008). In this study, the data obtained from Zeydan's paper (Zeydan, 2008) will be used for finding both the effect of some fibre, yarn and fabric parameters on the strength of jacquard woven mattress fabric and level configuration of parameters providing maximum fabric tensile strength. A new modelling methodology in the prediction of woven fabric strength will be introduced in this chapter and compared by using TDOE (Taguchi Design of Experiment), ANN, GA-ANN (Genetic Algorithm based Artificial Neural Network) Hybrid structure and multiple regression methodology.…”

“…Testing machine was set for a loading rate of 300 mm/min. All tests were performed under standard atmospheric conditions (per cent 65 +-2 relative humidity and 20+-28C temperature) and the samples were conditioned hours under such conditions for 24 before testing (Zeydan, 2008). Jacquard woven fabrics are widely used in various sections of upholstery industry, where mattress cover is one of them.…”

Reducing Mirror Slippage of Nightstand with Plackett-Burman DOE and ANN Techniques

“…Taguchi method has a wide usage in the engineering applications but criticized in the literature about not concerning with the interactions between factors while focusing on evaluation of main effects totally 7 . It is one of the most important tools that can be used for improving steps of Six Sigma loop 8 .…”

An Investigation On Air and Thermal Transmission Through Knitted Fabric Structures Using the Taguchi Method

“…Four controlled factors such as loop length, carriage speed, yarn input tension, and yarn count were chosen, and for each factor, three levels were considered. All the fabric samples were made in a 12-gauge (number of needles in 1 inch width of the machine) computerized flat knitting machine equipped best", "larger-the-better", or "smaller-the-better" and can be calculated using the Equations (3)-(5) [19][20][21].…”