Modeling the seam strength of denim garments by using fuzzy expert system

Sarkar, Joy; Faruque, Abdullah Al; Mondal, Moni Sankar

doi:10.1177/1558925021988976

Cited by 10 publications

(15 citation statements)

References 28 publications

(41 reference statements)

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“…if-then rules are used to represent the knowledge in a knowledge base system. These if-the statements relate the input variables and output variables [ 53 , 61 ]. Fuzzy rules base is classified as Mamdani and Sugeno [ 50 ].…”

Section: Methodsmentioning

confidence: 99%

“…Though ANN models usually require a huge number of datasets to operate accurately, at the same time if designed optimum, it can also demonstrate an excellent accuracy with a small amount of dataset [ 47 ]. On the other hand, fuzzy logic-based models are simple, require less data, cheaper in design cost, and are highly configurable [ 36 , 53 , 54 ]. Furthermore, the fuzzy logic-based model excels at property prediction, particularly with fewer datasets, which is beneficial for the industries [ 53 ].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, fuzzy logic-based models are simple, require less data, cheaper in design cost, and are highly configurable [ 36 , 53 , 54 ]. Furthermore, the fuzzy logic-based model excels at property prediction, particularly with fewer datasets, which is beneficial for the industries [ 53 ]. In addition, as the primary target users of this model are the textile engineers and designers, that's why adopting a simple or in other words less complex method is more convenient as they are generally not used to using high-end technologies like neural networks.…”

Section: Introductionmentioning

confidence: 99%

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Predicting the tearing strength of laser engraved denim garments using a fuzzy logic approach

Sarkar

Faruque

Khalil³

2022

Heliyon

Self Cite

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This research aims to develop a fuzzy logic-based model for predicting the warp way and weft way Tearing Strength (TS) of laser engraved denim garments concerning two of the most important laser parameters such as Dots Per Inch (DPI) and Pixel Time (PT). Laser engraving is a widely used approach in garment washing factories because of its lower health hazards, time efficiency, and accuracy than other processes. However, controlling the laser parameters is very important, as if the tearing strength of the treated garments falls lower than the tolerable limit, the garment might be rejected. In this study, the fuzzy logic-based method is used to develop a prediction model to determine the Tearing Strength of the laser engraved denim. The model exhibits the exact same trend for TS as the experimental findings, i.e., TS increases with the decrement of either DPI, PT, or both. Moreover, the Mean Relative Errors (%) for warp and weft way Tearing Strength was found to be 3.34 and 3.53, respectively, which are within the acceptable limits. The coefficient of determination (R 2 ) was found 0.98 (R ¼ 0.99) for both the warp and weft way Tearing Strength, and the result suggested that up to 98% of total changes in warp and weft way Tearing Strength can be explained by the model. From the results, it can be evident that the principle of the proposed model can satisfactorily be used in predicting the Tearing Strength of the laser engraved denim garments, which will be beneficial for the garment washing industry by eliminating a lot of existing trial and error approach to set process parameters and thus can play an important role in increasing the productivity by process optimization.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting the tearing strength of laser engraved denim garments using a fuzzy logic approach

Sarkar

Faruque

Khalil³

2022

Heliyon

Self Cite

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“…These parameters have to be maintained in order to maximize the product durability by ensuring a proper interaction between the fabrics and the seam [6][7][8]. Yassen studied the effect of sewing thread count, sewing needle size, stitch density, and fabric properties on seam strength and showed that both sewing and fabric parameters had a substantial impact on seam strength [9].…”

Section: Introductionmentioning

confidence: 99%

Seam Strength Prediction for Different Stitch Types Considering Stitch Density of Cotton Woven Fabrics

Islam¹,

Hossain²,

Repon³

et al. 2022

TLR

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Seam strength is critical in achieving the proper and adequate quality seam, which eventually determines the overall quality of any garment. The target of this research was to investigate exactly how different stitch density affected seam strength for different stitch types. Commercially available, 100% cotton plain weave structured woven fabric with mass per unit area of 270 g/m2 was used in this analysis. Two different stitch types, such as lock stitch (ISO#301) and chain stitch (ISO#401), along with four stitch densities like 8, 10, 12 and 14 per inch were utilized. The seam strength of the prepared samples was determined using a tensile strength tester followed by the ASTM D1683 standard procedure. The impact of independent variables on superimposed seam strength was statistically analyzed using a regression correlation with the help of SPSS software to construct a regression equation to predict seam strength before the manufacturing process. The study found a non-linear relationship between seam strength and stitch density. When the stitch density is too low, the seam strength is shown low as well, because the cloth cannot be held under tensile load. In a very compact woven fabric, structural jamming or intrinsic puckering occurs if it is too high. As a result, the best stitch density should be chosen to achieve the best seam strength. This study will help the manufacturers choose a suitable SPI for lock and chain stitches from polynomial regression equations before the sewing production of cotton woven fabric, which was not predicted by any researcher before. Therefore, the optimum stitch density can be chosen using regression equations to get the best result for seam strength.

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“…Among different soft computing-based approaches, the fuzzy inference-based prediction system has been successfully developed and utilized in the prediction of different fabric properties [ 25 , 31 , 32 , 33 , 34 ]. Besides, the color property [ 35 , 36 ], tearing strength, and seam strength of the garments [ 37 , 38 ] have also been predicted using the fuzzy inference-based prediction system. Although the fuzzy logic-based models exhibited excellent performance in most cases, it primarily relies on expert suggested rule base system, which can be subjective depending upon the expert.…”

Section: Introductionmentioning

confidence: 99%

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

Sarkar

Prottoy

Bari

et al. 2021

Heliyon

Self Cite

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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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Modeling the seam strength of denim garments by using fuzzy expert system

Cited by 10 publications

References 28 publications

Predicting the tearing strength of laser engraved denim garments using a fuzzy logic approach

Predicting the tearing strength of laser engraved denim garments using a fuzzy logic approach

Seam Strength Prediction for Different Stitch Types Considering Stitch Density of Cotton Woven Fabrics

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

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