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The aim of this paper was to predict the needle penetration force in denim fabrics based on sewing parameters by using the fuzzy logic (FL) model. Moreover, the performance of fuzzy logic model is compared with that of the artificial neural network (ANN) model. The needle penetration force was measured on the Instron tensile tester. In order to pla...
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Citations
... Larger sample sizes produce more accurate findings, and the size of the dataset has a substantial influence on how well ANN models predict the results. [35][36][37] ANN systems have demonstrated successful application in various applications within the textile industry. These applications include the classification of errors in textile operations, estimates of yarn quality parameters, classification of knitted and woven fabrics, estimation of fabric perception features, prediction of clothing comfort, determination of air permeability in knitted and woven fabrics, as well as estimation of fabric drape and mechanical properties such as static tear strength. ...
The present research compares a machine learning model with a statistical model, with specific emphasis on artificial neural networks and multiple linear regression models. The aim of this study is to forecast the thermal transmittance of a plain-woven cotton fabric using input data such as thread density measured in ends per inch, picks per inch, and fabric thickness. The artificial neural network is built using a network with feed-forward backpropagation, and the MATLAB software’s training function trainlm is used to modify its weight and basic values based on Levenberg–Marquardt optimization techniques. The sigmoid transfer function is used to set the layer output and measure network performance in terms of the root mean squared error, mean absolute error percentage, and coefficient of determination which were determined. For the artificial neural network prediction model, the root mean squared error and mean absolute error percentage were 1.05 and 3.132%, respectively, while the coefficient of determination was 0.9307. In contrast, the multiple linear regression prediction model had root mean squared error and mean absolute error percentage values of 2.98 and 8.97%, respectively, along with a coefficient of determination of 0.4727. The results reveal that the artificial neural network model outperforms the multiple linear regression model, showing superior accuracy and robustness in capturing the intricate interactions between important fabric parameters (ends per inch, picks per inch, and thickness) and thermal transmittance values. This research emphasizes the efficiency of artificial neural network modeling as a superior tool for forecasting thermal transmittance in textile applications rather than employing the time-consuming trial-and-error process for delivering significant insights for material engineering and energy-efficient design.
... When the studies on the use of the ANN method in ready-made clothing are examined It has been seen that the studies are related to predicting the fabric performance and fabric end use [11,12], design of smart sewing machines, and smart sewing environments [13][14][15], system identification and controller synthesis for sewing machine [16], fabric stitching inspection method proposal [17], sewing fault detection and classification [18], quality control of seams [19], control of sewing parameters [20], modelling and prediction of needle penetration force [21,22], predicting of sewing thread consumption [23], predicting of sewing performance [24][25][26], predicting of strength loss in threads [27], predicting of seam strength [28], prediction and rating of seam pucker [29,30], calculation of optimum fabric lays quantities [31], forecasting of cutting time [32], use of artificial intelligence in cutting and sewing [33]. ...
In this study, the results of the work distribution made with the TOPSIS method, which is frequently used in the distribution of work to the subcontractor workshops, were estimated using the Artificial Neural Networks Method (ANN). Here, the C* values used in the work distribution with the TOPSIS method were estimated by ANN. The correlation coefficient of the data obtained from the created model was found to be 99.99895% for learning. According to the results, it has been concluded that work distribution can be made by using the ANN method without making complex mathematical calculations in the distribution of work to the contract workshops.
... Some of them were accomplished in different fields of textile science and engineering. [30][31][32][33][34][35][36][37][38][39][40] Among modeling research studies, a semi-experimental fuzzy logic model has been implemented to predict acrylic cut-pile carpet thickness-loss under compression for carpet pile density, pile height, and pile yarn count, recently. 41 In this model, experimental data have been used for both construction of model knowledge base and model precision assessment. ...
Thickness-loss is the most common problem in carpet after static and dynamic loading. Pile yarn properties as well as carpet structural parameters are mainly responsible for carpet thickness-loss. In the present research, an advanced version of recently developed fuzzy logic model is introduced. The model is able to predict thickness-loss of polyester carpets based on carpet pile density, pile height, and pile yarn count. Experimental work was performed to provide data for model knowledge base. Genetic algorithm was employed to optimize the fuzzy logic model parameters. Finally, lowest possible thickness-loss value together with corresponding values of carpet and pile yarn parameters bring this result was defined, using developed model. Modeling results showed that the model attained correlation coefficients as 0.9932, 0.9911, 0.9950, and 0.9957 between predicted and experimental values of carpet thickness-loss after low and high dynamic loading and static loading with short and long relaxation times, respectively. On the other hand, model predictions in four new unsighted conditions have brought correlation coefficients as 0.82, 0.89, 0.88, and 0.90 for low and high dynamic loading and static loading after short and long relaxation times, respectively. These results denote acceptable reliability of new developed model. Eventually, it is defined that levels of 850, 7.5, and 957.5 for carpet pile density, pile height, and pile yarn linear density, respectively, bring minimum carpet thickness-loss.
... The subject of needle penetration force measurement has been studied by various researchers since the 1960s. These studies can be classified into three categories: the development of an instrument to measure penetration forces (Leeming and Munden, 1978;Carvalho et al., 2009;Ujevic et al., 2008), the investigation of parameters that influence the penetration forces (Gurarda and Meric, 2005), and prediction of penetration forces based on mathematical models (Lomov, 1998;Haghighat et al., 2014). ...
Purpose
This paper suggests a new method for determining the sewing damage that occurs in single jersey fabrics based on the calculation of the needle hole area. This paper also aims to investigate the effects of material type, sewing parameters, and repeated washing cycles on sewing damage by using this method.
Design/methodology/approach
Six knitted fabrics, differing in structure and raw material, were produced. Samples were sewn using different sewing parameters, and they were subjected to ten washing cycles. Values of average needle hole area, an objective indicator of sewing damage, were calculated before and after repeated washing cycles using image analysis software and were evaluated statistically.
Findings
The results showed that the average needle hole area calculated via the image analysis software effectively estimates sewing damage as the results obtained were compatible with those stated in the literature. Furthermore, fabric type, sewing direction, sewing thread type, and needle size significantly affected the sewing damage. However, stitch density did not affect the sewing damage. When the effect of washing cycles was compared, it was seen that washing leads to an increase in sewing damage.
Originality/value
A review of the existing literature shows that no previous study has evaluated sewing damage using image analysis software. This study proposes a novel objective method to determine the sewing damage that occurs in knitted fabrics.
... 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][30][31][32][33] Weave type ði1Þ ¼ To develop the fuzzy prediction model three fabric variables, weave type, weave density, and number of filaments, were used as input variables and fabric air permeability as an output variable. These fabric inputs were selected as they are the most significant factors on fabric air permeability. ...
The resistance of carpets to texture deformation is considered one of the most important properties that affects the quality of carpet. Static load is an essential factor that has a profound impact on carpets causing compression of carpet pile yarns. Mainly, two tests are available to determine the compressibility and resilience performance of carpets, namely thickness loss after brief moderate static loading and thickness loss after prolonged heavy static loading. Currently, the commercially available test devices have drawbacks, including a requirement for an additional apparatus to test performance, conducting tests manually which leads to personal faults, and inability of storing test data. In this study, a newly developed test device for measuring compressibility and resilience performance of carpet was designed and manufactured. The newly designed test device is statistically verified and capable of performing carpet thickness measurement, brief moderate static loading and prolonged heavy static loading tests automatically.
... Modeling based on fuzzy expert systems has been used in the field of dyeing, weaving, knitting, seam strength prediction, laser engraving, etc. 4,35,[38][39][40][41][42][43][44] Nevertheless, no significant work has been reported to demonstrate fuzzybased modeling in the field of industrial garment washing, especially in the case of bleach washing. Since washing is crucial in today's apparel world and bleach wash is the most challenging type of wash whose process parameters are hard to control, they demand extra precautions to maintain the quality of the final product. ...
... 38 These rules use an if-then statement to connect the input and output variables [24], [26], [28]. For example, for the inputs X and Y, and output Z those have the linguistic variables, namely low and medium for X and Y respectively, and medium for Z then the fuzzy expert rules 39,47 can be constructed as the following expression: ...
... If X is low, and Y is medium, then Z is medium. Between the two types of fuzzy rule base named Mamdani and Sugeno, 39,48,49 Mamdani rules have been used in this particular study. ...
The prime intention of this study is to develop and validate the efficiency of a fuzzy logic model to predict the tensile strength of bleach washed denim garments. Denim washing is nowadays a widely used technology to give a worn-out appearance to denim garments and thus add esthetic to that garments’ appearance and outlook. In the case of regular overall fading of denim, bleach washing is very popular. At the same time, it is also a matter of concern that the process optimization of bleach washing is critical as bleaching agents have an adverse effect on the fabrics. Among others, bleach concentration, time, and temperature are the primary process parameters in bleach washing, which directly impact the final strength of the treated denim garments. Though their relationship is nonlinear, a fuzzy expert-based system has been constructed to model and illustrate the complex relationship among bleach concentration, time, and temperature in the input side, whereas tensile strength is the output. Strength is the most critical parameter of the final denim product, as it dominates the serviceability of that garments. Therefore, predicting the tensile strength without the trial-and-error method, which is being practiced now, can be a blessing for industrial practitioners. With the help of a laboratory trial, the developed model has been evaluated, and it has been found that the mean relative error was 2.82 and 3.92 for warp and weft direction tensile strength, respectively. On the other hand, the comparison exhibited a coefficient of determination ( R ² ) of 0.99 for both warp and weft direction tensile strengths. The authors found that the performance of the developed model is satisfactory enough to predict the tensile strength of the bleach washed denim garments.
... As a result, these models provide noisy data with a lower level of precision [48][49][50]. In this regard, soft modeling methods based on intelligent techniques like Artificial Neuro Fuzzy Inference System (ANFIS) or ANN can substantially address the issue with reasonable accuracy [51]. ...
... J o u r n a l P r e -p r o o f and output variables [53,61]. Fuzzy rules base is classified as Mamdani and Sugeno [50]. In this paper, Mamdani rules have been used. ...
... In this study, for input-output variables, triangular-shaped membership functions are used because of their accuracy. At the same time, the Mamdani max-min inference mechanism and the center of gravity (COG) defuzzification system [50] was used. ...
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 (R²) was found 0.98 (R = 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.
... Predicting needle penetration force (NPF) in the sewing process can invade the needle breakage and consequently promote the process efficiency and product quality. Related works have been reported by Haghighat et al. comparatively using ANN and multiple linear regression (MLR) [55], as well as fuzzy logic and ANN [56]. The considered input variables are composed of the No. of fabric layers, needle size, weave pattern, and fabric weight in the comparison of In addition to the NPF, thread tension is also mentioned above that the affects the sewability in the sewing process. ...
As the need for quickly exploring a textile manufacturing process is increasingly costly along with the complexity in the process. The development of manufacturing process modeling has attracted growing attention from the textile industry. More and more researchers shift their attention from classic methods to the intelligent techniques for process modeling as the traditional ones can hardly depict the intricate relationships of numerous process factors and performances. In this study, the literature investigating the process modeling of textile manufacturing is systematically reviewed. The structure of this paper is in line with the procedure of textile processes from yarn to fabrics, and then to garments. The analysis and discussion of the previous studies are conducted on different applications in different processes. The factors and performance properties considered in process modeling are collected in comparison. In terms of inputs’ relative importance, feature selection, modeling techniques, data distribution, and performance estimations, the considerations of the previous studies are analyzed and summarized. It is also concluded the limitations, challenges, and future perspectives in this issue on the basis of the summaries of more than 130 related articles from the point of views of textile engineering and artificial intelligence.
... A fuzzy expert system is an artificial intelligence derived from fuzzy set theory established by Zadeh in 1965 [20,21,24]. The basic components of a fuzzy expert system are a fuzzifier, a fuzzy rule base, an inference engine and a defuzzifier as depicted in Figure 2. ...
... This value is called a membership value. Among various forms of membership functions, the triangle membership function is the simplest and most frequently used due to its accuracy [20,24]. ...
... The fuzzy rules are the heart of fuzzy modelling and are expressed by if-then statements that narrate the input variables in the antecedent part and output variables in the consequent part, which determines the input-output relationship of the model [19,20,24]. As an expression, when a fuzzy model with two inputs and one output involves n fuzzy rules, the development of fuzzy rules can be presented as follows: ...
The application of cross-linking resin is an effective method for improving and controlling dimensional stability, such as the shrinkage of viscose single jersey knits. However, such treatment often leads to a significant deterioration in the bursting strength of treated fabrics. In this regard, resin treatment using a softening agent can be an additional potential solution for retaining the bursting strength of treated fabrics. Resin treatment is one kind of chemical finishing process that inhibits cellulosic textile fibre swelling during wetting, provides fibre resistance to deformation and prevents shrinkage. The key objective of this study was to model the effect of resin-finishing process variables for predicting the shrinkage control and bursting strength of viscose single jersey knitted fabrics. The MATLAB (Version 8.2.0.701) fuzzy expert system was used to model the optimum resin and softener concentrations, as well as the best curing time for the prediction of maximum shrinkage control with a minimum loss in fabric bursting strength. The optimal process variables were found to be a resin concentration of 75 g/l, a softener concentration of 45 g/l and a curing time of 225 seconds. The fuzzy expert model developed in this study was validated using experimental data. It was found that the model has the ability and accuracy to predict fabric shrinkage and bursting strength effectively in the non-linear field.
... 29 These rules relate the input and output variables and are operated by if-then statement. 30,32,34 For instance, for two inputs A and B, and one output C having linguistic variables of low and medium for A and B respectively and medium for C then the development of fuzzy inference rules 30,32,34,36 can be presented as follows: ...
... 38 The defuzzification interface converts the fuzzy output into precise crisp numeric values by combining the conclusions made by the decision making logic. 36,39 There are several methods of defuzzification interface, such as centroid, center of sum, mean of maxima and left-right maxima. 40 However, among these the most used defuzzification method is the center of gravity (centroid) defuzzification method, as this operator assures a linear interpolation of the output between the rules. ...
... The triangular-shaped membership functions have shown more accuracy in this aspect. Mamdani maxmin inference mechanism and the center of gravity defuzzification method 33,35,36 have been applied in this research work. ...
The main purpose of this study is to predict and develop a model for forecasting the Seam Strength (SS) of denim garments with respect to the thread linear density (tex) and Stitches Per Inch (SPI) by using a Fuzzy Logic Expert System (FLES). The seam strength is an important factor for the serviceability of any garments. As seams bound the fabric pieces together in a garment, the seams must have sufficient strength to execute this property even in the unexpected severe conditions where the garments are subjected to loads or any additional internal or external forces. Sewing thread linear density and number of stitches in a unit length of the seam are the two of the most important factors that affect the seam strength of any garments. But the relationship among these two specific variables and the seam strength is complex and non-linear. As a result, a fuzzy logic based model has been developed to demonstrate the relationship among these parameters and the developed model has been validated by the experimental trial. The coefficient of determination ( R ² ) was found to be 0.98. The mean relative error also lies withing acceptable limit. The results have suggested a very good performance of the model in the case of the prediction of the seam strength of the denim garments.
