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 plan the fuzzy logic model, the sewing needle size, number of fabric layers and fabric weight were taken into account as input parameters. The output parameter is needle penetration force. In addition, the same parameters and data are used in artificial neural network model. The results indicate that the needle penetration force can be predicted in terms of sewing parameters by using the fuzzy logic model. The difference between performance of fuzzy logic and neural network models is not meaningful ( RFL=0.971 and RANN=0.982). It is concluded that soft computing models such as fuzzy logic and artificial neural network can be utilized to forecast the needle penetration force in denim fabrics. Using the fuzzy logic model for predicting the needle penetration force in denim fabrics can help the garment manufacturer to acquire better knowledge about the sewing process. As a result, the sewing process may be improved, and also the quality of denim apparel increased.

This paper investigates the sewability of the woven denim fabrics based on needle penetration force (NPF). For this purpose, the effects of fabric weight, number of fabric layers, needle size, and the interaction effect of these parameters on NPF in twill denim fabrics were investigated. In addition, the influence of weave pattern on NPF was studied. The statistical analysis results show that NPF is influenced by these parameters. Fabric weight has a greater effect on NPF than other parameters. With increasing fabric weight, number of fabric layers, and needle size, the NPF increases. The trend of this increase is nonlinear as predicted by a cubic regression equation. The fabric sewability is also influenced by the mentioned parameters. The fabric sewability becomes poor with increasing fabric weight, needle size, and number of fabric layers. Generally, lighter fabrics sewn with finer needles have better sewability.

The aim of the present study is to produce elastic core/cotton spun yarns in a friction spinning (Dref-II) system and then to investigate the effect of spandex filament draw ratio on the physical and mechanical properties of produced yarns. First, a positive feed device was designed and developed to adjust the spandex draw ratio and then mounted on the friction spinning machine. To maintain a constant yarn count at various draw ratios, the proportion of cotton sheath fibers should be increased. The yarns with larger proportions of cotton fibers showed lower twist. The elastic core spun yarns have lower hairiness in comparison with 100% cotton yarns in the present experiments. The theoretical and experimental results indicate that the tenacity of elastic core spun yarn increases as the spandex draw ratio increases. While the elongation of the elastic core spun yarns is the same as 100% cotton yarn at low spandex draw ratios of less than 3.75, when the spandex draw ratios exceed 3.75, the elongation of the elastic core spun yarn increases sharply.

Purpose -This paper aims to predict the needle penetration force (NPF) in denim fabrics using the artificial neural network (ANN) and multiple linear regression (MLR) models based on the effects of various sewing parameters. Design/methodology/approach -In order to design the ANN and MLR models, four parameters including fabric weight, number of fabric layers, weave pattern, and sewing needle size are taken into account as the input parameters and NPF as the output parameter. According to these parameters, 140 samples of data were resulted. Each sample was tested five times. From these 140 data (input-output data pairs), 112 were used for training the ANN and MLR models and 28 samples were used to test the performance of ANN and MLR. Also, the NPF was measured on the Instron tensile tester to simulate sewing process. Findings -The results indicated that the NPF in denim fabrics can be well predicted in terms of sewing parameters by using ANN and MLR models, in which the ANN model exhibits greater performance than MLR (RANN ¼ 0.989 . RMLR ¼ 0.901).Research limitations/implications -The NPF measurement method is limited at low speed. Originality/value -Using the ANN model for forecasting NPF in denim fabrics can help the garment manufactures to produce high-quality denim products and improve the sewing process through reducing seam damage. The NPF could be also measured in the cycle loading conditions similar to sewing machine process by using a special designed tools mounted on the Instron tensile tester.

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