Artificial Neural Network

Artificial neural network (ANN) is a computational powerful data modeling tool in artificial intelligence (Hatua et al., 2014; Hussain et al., 2015; Ngai et al., 2014) that has been applied successfully to various disciplines including textile and dyeing area for different working conditions. The application of ANN related to the subject of this work can be summarizes as follows:

Hussain et al., (2015) applied ANN model to predict the wrinkle recovery of polyester/cotton blended woven fabrics by taking input variables as warp and weft yarn linear densities, ends/25 mm and picks/25 mm.

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Moreover, authors compared the ANN model performance with ANFIS model in terms of prediction accuracy and found that ANN model exhibits slightly better performance than the ANFIS Model.

Moezzi et al., (2015) established ANN model to predict the tensile properties of UV degraded nylon66/polyester woven fabric at different levels of exposure time. The results obtained from the ANN model were then compared with the experimental results. Authors proved that developed ANN model performs excellent in prediction.

Haghighat et al., (2014) developed ANN model to investigate the influence of sewing needle size, number of fabric layer and fabric weight on the needle penetration force in woven denim fabrics. The results signify that the needle penetration force in various denim fabrics can be predicted with high accuracy using ANN model. In addition, authors showed that the effects of number of fabric layer and fabric weight on the needle penetration force are much more profound than needle size. In another study, Haghighat et al., (2012b) discovered ANN prediction model to study the hairiness of polyester-viscose blended yarn. Authors further stated that ANN model can be applied as a decision making support tool for the production engineer to select and adjust the appropriate spinning process parameters for the production of high quality yarn.

Hatua et al., (2014) developed ANN model for the prediction of ultraviolet protection factor of polyester-cotton blended woven fabrics by taking proportion of polyester in weft yarn, weft count and pick density as input variables. Further, author compared the developed ANN model with ANFIS model in terms of prediction accuracy and found that both models have ability in prediction with high accuracy.

Jamshaid et al., (2013) presented ANFIS model to predict the bursting strength of plain knitted fabrics based on the yarn tenacity, knitting stitch length and fabric GSM as input variables. In this study, they explained that the effect of stitch length and fabric GSM is not linear on the fabric bursting strength.

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Kan et al., (2013) used ANN model to predict the color propperties of 100% cotton denim fabric taking input variables treatment temperature, treatment time, pH, mechanical agitation and fabrics yarn twist level. The results obtained from ANN model were then compared with experimental results and found that color properties could be predicted perfectly with the aid of ANN model.

Azimi et al., (2013) discovered ANN methodology to predict the effect of first heater temperature, setting overfeed and D/Y on the tenacity of set yarns and effect of twist texturing speed and first heater temperature on the crimp stability of stretch yarn.

Unal et al., (2012) applied ANN prediction model to investigate the effects of yarn count, yarn tenacity, yarn unevenness, number of wales and coreses on the single jersey cotton knit fabrics bursting strength and air permeabilty. In the same study, authors develop regression model to compare the ANN model performance and found that ANN model is superiorto regression model.

Shams- Nateri, (2011) investigated the effects of types and number of membership function on the performance of digital camera based ANFIS technique to measure the color properties of polyester fabrics.

Khataee et al., (2011) reported an ANN model to predict the performance of biological process and investigate effect of temperature, pH, initial dye concentration, reaction time amount of algae on biological de-coloration efficiency.

Rolich et al., (2010) applied ANN model to investigate the effects of weft yarn density, warp yarn density, mass per unit area and thickness of fabrics on the tensile properties of woven fabrics.

Furferi and Gelli, (2010) discovered ANN model for predicting the yarn strength based on fiber properties such as length, strength and fineness. Authors proved that developed ANN model may be considered a practical method for assessing the yarn strength comparison with linear regression model.

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Yuen et al., (2009) developed and design a new novel method for three layers BP neural network to investigate and classify fabrics stitching defects automatically.

Ertugrul and Ucar, (2000) applied ANN and ANFIS intelligent models for the prediction of bursting strength of cotton plain knitted fabrics by taking fabric weight, yarn breaking strength and yarn breaking elongation as input parameters.

The ANN and ANFIS models execute superior prediction accuracy in non-linear complex ground. Nevertheless, these ANN and ANFIS models are trained using massive amounts of noisy experimental data for parameters optimization which are labor intensive and time consuming process to accumulate from the textile and dyeing industries. Further, ANN and ANFIS models work as black box and there is no precise amplification of the nature of non-linearity between input-outputs (Jamshaid et al.,

2013; Majumdar and Ghosh, 2008)

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In addition, ANN does not tell the core logic based on which decisions are taken (Hatua et al., 2014). Comparison between Taguchi method, statistical method, ANN and Genetic algorithm (GA) optimization methods and techniques has been presented in Table 2.3.

In document Dyeing process parameter optimization and quality characteristics modeling for viscose blended knitted fabrics / Md Ismail Hossain (Page 77-80)