Predicting properties of single jersey fabrics using regression and artificial neural network models

Ünal, Pelin Gürkan; Üreyen, Mustafa Erdem; Mecit, Diren

doi:10.1007/s12221-012-0087-y

Cited by 32 publications

(23 citation statements)

References 19 publications

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“…al. applied regression and artificial neural network for the prediction of the bursting strength and air permeability of single jersey knitted fabrics [20]. Minapoora et.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the performance properties of knitted fabrics containing elastane

Eryürük

Kalaoğlu²

2016

International Journal of Clothing Science and Technology

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Purpose – Knitted fabrics containing elastane provide high level of comfort and ease of usage because of the elastic and drape properties over the body. Knitted fabrics respond to every movement of the body and return back to its original shape easily so they are used widely for apparel production. The most important properties required from the elastic knitted garments are wear comfort, fit, breathability and durability. The purpose of this paper is to analyse the effect of elastane yarn count and ground yarn count on the performance properties of 12 single jersey knitted fabrics were analysed after dying. Design/methodology/approach – The research design for this study consists an experimental study. In all, 12 fabrics containing half plating and full plating elastane were produced using 30/1-40/1 Ne yarn counts. Bursting strength, stretch recovery, residual extension, air permeability, spirality and drape properties of fabrics were evaluated. Findings – As a result of study it was found a certain effect as the elastane amount and count changed. For all types of knitted fabrics, bursting strength values increased and fabric spirality values decreased as the elastane amount and elastane yarn count increased. Also it was found a significant relationship between elastane amount and count with air permeability, spirality, bursting strength and drape. Originality/value – As a result of the literature review, it was seen that the effects of elastane amount, elastane yarn count and fabric yarn count on the performance properties of knitted fabrics has not been studied broadly.

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“…al. applied regression and artificial neural network for the prediction of the bursting strength and air permeability of single jersey knitted fabrics [20]. Minapoora et.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the performance properties of knitted fabrics containing elastane

Eryürük

Kalaoğlu²

2016

International Journal of Clothing Science and Technology

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“…The advantage of ANNs is the ability of representing complex relationships directly from the data being modeled, while their representation (modeling) is always nonlinear [12][13][14] . Many researches were done in the textile industry to predict the properties of yarns, woven and nonwoven fabrics and many other characteristics of textile materials 10,[13][14][15][16][17][18][19][20][21][22][23][24][25] . Among these, just few studies are devoted to melt spinning and drawing of synthetic fibers.…”

Section: Introductionmentioning

confidence: 99%

Predicting the physical properties of drawn Nylon-6 fibers using an artificial-neural-network model

Rahbar¹,

Vadood²

2015

Mater. Tehnol.

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Low-oriented nylon-6 fibers were drawn in a multistage drawing process, during which the number of drawing steps and the temperature of each step were changed. The physical properties of these fibers were measured and compared with the values predicted by a multiple-linear-regression model. Moreover, six input variables and four output variables were used in an artificial neural network (ANN) to establish the logical relationships between the inputs and outputs. Attempts were also made to determine the effective parameters for each physical property and explain the observed trends. The results showed that the models based on the ANN performed well and provided stable responses in predicting combined interactions between independent variables. Keywords: drawing process, artificial neural network, modeling, physical properties Malo orientirana vlakna najlon 6 so bila vle~ena z ve~stopenjskim postopkom, pri~emer se je pri vsakem vleku spreminjala stopnja vle~enja in temperatura. Izmerjene vrednosti teh vlaken so bile primerjane z vrednostmi, napovedanimi z modelom multivariantne linearne regresije. Poleg tega je bilo v umetni nevronski mre`i (ANN) uporabljenih {est vhodnih spremenljivk in {tiri izhodne, da bi ugotovili logi~ne odvisnosti med vhodnimi in izhodnimi spremenljivkami. Posku{alo se je ugotoviti u~inkovite parametere za vsako fizikalno lastnost in razlo`iti opa`ene tendence. Rezultati so pokazali, da so modeli na osnovi ANN dobri in ponujajo stabilne odgovore pri predvidevanju kombiniranih interakcij neodvisnih spremenljivk. Klju~ne besede: postopek vle~enja, umetna nevronska mre`a, modeliranje, fizikalne lastnosti

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“…Testing tensile and tearing strength in the wale and coarse directions in knitted fabrics are not suitable because of the structural properties; hence, testing bursting strength turns out to be extremely important particularly for knitted fabrics before manufacturing. Generally, the bursting strength test is conducted to evaluate the fabric's capability to withstand multiaxial stresses without breaking off [1,4,6,7].…”

Section: Introductionmentioning

confidence: 99%

“…Basically, strength of fabrics depends on starting fabric forming materials such as yarn properties and fabric density. The literature review revealed that various studies have been reported on the factors affecting the bursting strength of knitted fabric including yarn type, yarn count, yarn tenacity, yarn breaking elongation, yarn breaking strength, yarn twist, yarn evenness, fabrics GSM, fabric wale and courses, knitting stitch length, cover factor, tightness factors, and relaxation treatment [1,4,[6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Mechanical Properties of Viscose/Lycra Knitted Fabrics Using Fuzzy Technique

Hossain

Choudhury

Mamat

et al. 2016

Advances in Fuzzy Systems

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The main objective of this research is to predict the mechanical properties of viscose/lycra plain knitted fabrics by using fuzzy expert system. In this study, a fuzzy prediction model has been built based on knitting stitch length, yarn count, and yarn tenacity as input variables and fabric mechanical properties specially bursting strength as an output variable. The factors affecting the bursting strength of viscose knitted fabrics are very nonlinear. Hence, it is very challenging for scientists and engineers to create an exact model efficiently by mathematical or statistical model. Alternatively, developing a prediction model via ANN and ANFIS techniques is also difficult and time consuming process due to a large volume of trial data. In this context, fuzzy expert system (FES) is the promising modeling tool in a quality modeling as FES can map effectively in nonlinear domain with minimum experimental data. The model derived in the present study has been validated by experimental data. The mean absolute error and coefficient of determination between the actual bursting strength and that predicted by the fuzzy model were found to be 2.60% and 0.961, respectively. The results showed that the developed fuzzy model can be applied effectively for the prediction of fabric mechanical properties.

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Predicting properties of single jersey fabrics using regression and artificial neural network models

Cited by 32 publications

References 19 publications

Analysis of the performance properties of knitted fabrics containing elastane

Analysis of the performance properties of knitted fabrics containing elastane

Predicting the physical properties of drawn Nylon-6 fibers using an artificial-neural-network model

Predicting the Mechanical Properties of Viscose/Lycra Knitted Fabrics Using Fuzzy Technique

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