An optimal artificial neural network system for designing knit stretch fabrics

Alibi, Hamza; Fayala, Faten; Bhouri, Naoufel; Jemni, Abdelmajid; Zeng, Xiaoyang

doi:10.1080/00405000.2012.756134

Cited by 9 publications

(5 citation statements)

References 22 publications

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“…Over the last few decades, a new approach for modeling was presented known as artificial neural network (ANN) and used widely for prediction. There are many published works about using ANN in different areas of textile engineering (Alibi, Fayala, Bhouri, Jemni, & Zeng, 2013;Almetwally, Idrees, & Hebeish, 2014;Çelik, Dülger, & Topalbekiroğlu, 2013;Minapoor, Ajeli, Hasani, & Shanbeh, 2012;Naghashzargar, Semnani, Karbasi, & Nekoee, 2013;Semnani & Vadood, 2010;Shabaridharan & Das, 2013a, 2013bVadood, Semnani, & Morshed, 2011). However, the ANN model contains various parameters directly affect the prediction accuracy and should be optimized to obtain the highest prediction accuracy.…”

Section: Please Scroll Down For Articlementioning

confidence: 97%

Developing a hybrid artificial neural network-genetic algorithm model to predict resilient modulus of polypropylene/polyester fiber-reinforced asphalt concrete

Vadood

Johari

Rahai

2014

The Journal of The Textile Institute

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Up to now various kinds of fibers are used to improve the hot mix asphalt (HMA) performance, but a few works have been undertaken on the hybrid fiber-reinforced HMA. Therefore, in this paper, the resilient modulus of the modified HMA samples using polypropylene and polyester fibers (hybrid and single modes) was evaluated and modeled by regression method and artificial neural network (ANN). As ANN includes different parameters such as the number of neurons in hidden layer influenced on the prediction accuracy, genetic algorithm (GA) was used to optimize the ANN parameters. Also, GA parameters were optimized using the trial and error method such as the population size. The obtained results indicated that the optimized ANN with two hidden layers and two neurons in each hidden layer can predict the resilient modulus of fiber-reinforced HMA with high accuracy (correlation coefficient: .96).

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confidence: 97%

Developing a hybrid artificial neural network-genetic algorithm model to predict resilient modulus of polypropylene/polyester fiber-reinforced asphalt concrete

Vadood

Johari

Rahai

2014

The Journal of The Textile Institute

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“…The prediction of clothing comfort can be described as a [33] Air permeability (l/m 2 /s) NF G07-111 Thermal conductivity (W/m·K) ; Thermal resistance (m 2 K/W) [26,27,25,23] Relative water vapor permeability (%) [34,35,36] Thermal absorbitivity (W·s 1/2 /m 2 ·K). Thermal diffusivity (×10 -7 ) (m 2 /s) [37,38] Heat transfer coefficient (W/m 2 o C) [39] progressive process, which involves three fundamental elements: 1) structural properties of yarn and fabric, 2) fabric functional properties and 3) subjective comfort evaluation.…”

Section: Theoretical Model Of Thermal Clothing Comfort Predictionmentioning

confidence: 99%

“…On the other hand, neural networks show tremendous success in many fields. Artificial Neural Networks (ANN) have been widely used in many fields related to the thermal environment and comfort [21][22][23][24][25][26][27]. Wong proposes an ANN model to predict an overall comfort perception from 10 individual sensory perceptions [28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

A new hybrid artificial intelligence approach to predicting global thermal comfort of stretch knitted fabrics

et al. 2015

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Today numerous consumers consider thermal comfort to be one of the most significant attributes when purchasing textile and apparel products, so there is a need to develop a model able to simulate objectively the consumers' perception. The global thermal comfort of stretch knitted fabrics is a multi-criteria phenomenon that requires the satisfaction of several properties at the same time. In this paper, we used the desirability functions to evaluate the satisfaction degree of global thermal comfort. Statistical method was used to investigate the interrelationship among knit thermo-physical properties, and group them into factors. Two models of artificial neural network (general and special) have been set up to predict the global thermal comfort from structural parameters (inputs) of knitted fabrics made from pure yarn cotton (cellulose) and viscose (regenerated cellulose) fibers and plated knitted with elasthane (Lycra) fibers. A virtual leave one out approach dealing with over fitting phenomenon and allowing the selection of the optimal neural network architecture was used. By combining the strengths of statistics and fuzzy logic (data reduction and information summation) also a neural network (self-learning ability), hybrid model was developed to simulate the consumer thermal comfort perception. After that, ANN model is inverted. With a required output value and some input parameters it is possible to calculate the unknown optimum input parameter. Finally, this forecasting can help industrials to anticipate the consumer's taste. Thus, they can adjust the knitting production parameter to reach the desired global thermal comfort to satisfy this consumer.

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“…One of the key factors that influence the stretch properties of knitted fabrics is the type of yarn used, 25 the type of knit structure, 26,27 and their structural parameters. 28 The presence of missedknit stitches diminishes the elasticity of the fabric course-wise but enhances it wale-wise. The examination of elastic recovery demonstrated a decreasing discrepancy in recovery values among the knitted fabrics with knit loops and miss stitches as time progressed, suggesting a more uniform and consistent recovery performance.…”

Section: Introductionmentioning

confidence: 99%

The effect of miss stitches on the dimensional and stretch properties of double weft cotton/flax knitted fabrics

Bukhonka,

Kyzymchuk

2023

Journal of Engineered Fibers and Fabrics

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This research aims to investigate the influence of single miss stitches on the structural characteristics, dimensional stability, and stretch properties of double weft cotton/flax knitted fabrics. The study encompasses nine fabric variants with different numbers of knit and miss stitches in the repeat, alongside with half Milano rib fabric. All knitted fabrics are produced using a 2-ply 25x2tex x 2 cotton/flax blend yarn (70% cotton, 30% flax) on a 10 -gage flat-bed knitting machine. The comprehensive analysis encompasses crucial structural properties after dry relaxation for 10 days and after four washing cycles. Also, both the dimensional properties and stretch properties were examined b lengthwise and widthwise. It was found that the lengthwise dimensional changes for all fabrics were significantly greater than widthwise. The interlooping repeat significantly affects the widthwise fabric shrinkage: an increase in the percentage of missed stitches in the repeat leads to a decrease in the widthwise shrinkage. The full lengthwise deformation of studied fabrics ranges between 15% and 25%, while the widthwise value reaches up to 63% and depends on miss stitches repeat. An Increasing the percentage of missed stitches in the repeat leads to a decrease in the widthwise stretching due to the floats’ positioning in this direction. Fabrics with the same percentage of miss stitches in the repeat show similar levels of structural characteristics, shrinkage and deformations. In brief, this research offers valuable insights into the properties of double knitted fabrics with different single miss stitches. Understanding how these stitches influence fabric characteristics can greatly optimize textile design and manufacturing for both fashion and technical textiles.

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An optimal artificial neural network system for designing knit stretch fabrics

Cited by 9 publications

References 22 publications

Developing a hybrid artificial neural network-genetic algorithm model to predict resilient modulus of polypropylene/polyester fiber-reinforced asphalt concrete

Developing a hybrid artificial neural network-genetic algorithm model to predict resilient modulus of polypropylene/polyester fiber-reinforced asphalt concrete

A new hybrid artificial intelligence approach to predicting global thermal comfort of stretch knitted fabrics

The effect of miss stitches on the dimensional and stretch properties of double weft cotton/flax knitted fabrics

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