The Prediction of Needle Penetration Force in Woven Denim Fabrics Using Soft Computing Models

Haghighat, Ezzatollah; Najar, Saeed Shaikhzadeh; Etrati, Seyed Mohammad

doi:10.1177/155892501400900406

Cited by 15 publications

(33 citation statements)

References 16 publications

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“…A Mamdani max-min inference approach and the center of gravity defuzzification method have been applied in this research. The following functions are used in order to make the fuzzification of the used factors: 16,19,29–33 …”

Section: Methodsmentioning

confidence: 99%

Predicting air permeability of multifilament polyester woven fabrics using developed fuzzy logic model

Alsayed

Çelik

Kaynak

2020

Textile Research Journal

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The number of filaments in yarn cross-section, weave density, and weave type are considered the most important factors that affect the property of air permeability of multifilament woven fabrics. Microfilament yarns significantly affect the air permeability property of this type of fabric because of the low porosity between the filaments. This study deals with the development of a fuzzy logic model for predicting the air permeability of multifilament polyester woven fabrics produced from conventional and microfilament yarns. The polyester multifilament yarns used in this study were produced with three different microfilament fineness and two conventional filament fineness levels. The woven fabric samples used in this study were obtained in three weave types: plain, twill, and satin, and with five different weave densities. In accordance with the experimental test results, both regression analysis and fuzzy logic system were built. The air permeability results generated from the developed fuzzy model and the regression equations were compared with the experimental values. Satisfactory and accurate prediction results were obtained with the developed fuzzy logic model. The mean absolute error of the fuzzy model and regression analysis were found to be 2.32%, 12.59%, respectively. Therefore, it was confirmed that the fuzzy model was superior in predicting air permeability.

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Section: Methodsmentioning

confidence: 99%

Predicting air permeability of multifilament polyester woven fabrics using developed fuzzy logic model

Alsayed

Çelik

Kaynak

2020

Textile Research Journal

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“…Fuzzifier. Fuzzifier is the first block in the fuzzy modeling that converts all numeric inputs-outputs into fuzzy sets within a range from 0 to 1 such as low, medium, and high by using membership functions [12]. There are different forms of membership function such as triangle, trapezoid, and Gaussian [16].…”

Section: Methodsmentioning

confidence: 99%

“…Besides, fuzzy rules are the heart of the fuzzy logic expert system which determines the input-output relationship of the model [9,10]. The fuzzy rule base can be divided into two classes, namely, the Mamdani and Sugeno [12,17].…”

Section: Methodsmentioning

confidence: 99%

“…Haghighat et al have cited that, in many circumstances, for a system whose output is a fuzzy set, it is essential to aggregate the fuzzy sets into a single fuzzy set by aggregation method [12]. Finally, using (7) and (14) with Table 1 and Figure 6, the crisp output of bursting strength is found to be 330 kPa.…”

Section: Development Of Fuzzy Prediction Modelmentioning

confidence: 99%

“…The key purpose of this work is to construct fuzzy intelligent model for predicting the bursting strength of viscose/ lycra plain knitted fabrics as a function of knitting stitch length, yarn count, and yarn tenacity, which is not reported in the published literature. extension of crisp logic derived from fuzzy mathematical set theory developed by Zadeh in 1965 [9,12,13]. In crisp logic, such as binary logic, variables are true or false, black or white, and 1 or 0.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Comparison of ANFIS and ANN modeling for predicting the water absorption behavior of polyurethane treated polyester fabric

Sarkar

Prottoy

Bari

et al. 2021

Heliyon

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Nowadays, the polyurethane and its derivatives are highly applied as a surface modification material onto the textile substrates in different forms to enhance the functional properties of the textile materials. The primary purpose of this study is to develop prediction models to model the absorption property of the textile substrate using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) methods. In this study, polyurethane (PU) along with acrylic binder was applied on the dyed polyester knitted fabric to develop and validate the prediction models. Through the morphological study, it was evident that the solution prepared with the polyurethane and the acrylic binder was effectively coated onto the fabric surface. The ANFIS model was constructed by considering binder (ml) and PU (%) as input parameters, whereas absorbency (%) was the only output parameter. On the other hand, the system was trained with 70% data for constructing the ANN model whereas testing and validation were done with 15% data, respectively. To train the network, feed-forward backpropagation with Levenberg–Marquardt learning algorithm was used. The coefficient of determination (R 2 ) was found to be 0.98 and 0.93 for ANFIS and ANN model, respectively. Both prediction models exhibited an excellent mean absolute error percentage (0.76% for the ANFIS model and 1.18% for the ANN model). Furthermore, an outstanding root-mean-square error (RMSE) of 0.61% and 1.28% for ANFIS and ANN models was observed. These results suggested an excellent performance of the developed models to predict the absorption property of the polyurethane and acrylic binder treated fabric. Besides, these models can be taken as a basis to develop prediction models for specific types of functional applications of the textile materials to eliminate heaps of trial and error efforts of the textile industries, which eventually be helpful in the scalable production of functional textiles.

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The Prediction of Needle Penetration Force in Woven Denim Fabrics Using Soft Computing Models

Cited by 15 publications

References 16 publications

Predicting air permeability of multifilament polyester woven fabrics using developed fuzzy logic model

Predicting air permeability of multifilament polyester woven fabrics using developed fuzzy logic model

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

Comparison of ANFIS and ANN modeling for predicting the water absorption behavior of polyurethane treated polyester fabric

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