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Presents the influence of seam length, normal stitching velocity of a sewing machine and a working method on stitching velocity of sewing. Results show that better stitching velocities of sewing are gained by longer length of seams and higher than normal stitching velocities of a sewing machine. Reveals the working method and type of feeding of material affect the achievement of higher stitching velocities.

The purpose of this paper is to establish the regression equation based upon a set of samples prepared through structured design of experiment and form a prediction model for prediction of the areal density gram per square meter (GSM) of the embroidered fabrics and study the influence of basic input parameters.

Embroidery samples are prepared taking input parameters as GSM of the base fabric, linear density of the embroidery thread and stitch density of the embroidery design. Three levels of values are identified for each of the input parameters. Taguchi and Box-Behnken experiment design principles are used to prepare two sets of samples. Linear multiple regression is used to determine the prediction equations based upon each of the two sets and the combined set as well. Prediction equations are statistically verified for the prediction accuracy. Also, surface curves are prepared to study the influence of embroidery parameters on the GSM.

It is found that all the three prediction models developed in this study can predict with a very satisfactory level of accuracy. However, the regression equation based upon the data set prepared according to Taguchi experiment design is emerged as the prediction model with highest level of prediction accuracy. Corresponding equation coefficients and several three-dimensional surface curves are used to study the influence of embroidery parameters and it is found that the stitch density is the most influential input parameter followed by stitch length and the GSM of base fabric.

This can be used to assess the GSM of embroidered fabrics before starting the actual embroidery process. So, this model can help the embroidery designers significantly to pre-estimate the GSM of the embroidered fabrics and select the design parameters accordingly. Also, this model can be a useful tool for estimation of thread consumption and thread cost in embroidery.

The input parameters used here are very basic parameters related to design and materials, which can be easily available. And also, a simple linear multiple regression is used to make the prediction equation simple and easy to use. So, this model can help the embroidery designers or garment designers to select/adjust the embroidery parameters and thread parameters accordingly in the planning and designing stage itself to ensure that the GSM of embroidered fabrics remains within desirable range. Also, this prediction model developed hereby may be a very useful tool for estimation of the consumption and cost of embroidery threads.

This paper presents a very fundamental study to reveal the effect of embroidery parameters on the GSM, through development of regression equations. It can help future researchers in optimizations of input parameters and forming a technical guideline for the embroidery designers for selection of the design parameters for a desired GSM of embroidered fabric.

Sewing is the most widely used and preferred method for manufacturing clothing products for extreme weather conditions and other industrial insulation systems. Multiple layers of functional fabrics in combination with insulation materials are used to thermally insulate precious body heat from its surrounding cold environment. The sewing process fixes the insulation material between the fabric layers. During conventional sewing, the insulation material is compressed along the stitch line. With the compression of the insulation material, entrapped air is forced to leave the insulation material internal structure, and heat loss occurs along the entire length of the stitch line. It results in the deterioration of thermal properties of the end product along the stitch line.

The amount of air, which is a decisive factor for thermal properties of any insulation system, was investigated at the level of a unit stitch length of a lockstitch. Conventional microscopy methods are not suitable to study the compression along the stitch line. With the help of X-ray tomography, the three-dimensional data of a stitch was taken and studied to measure the volume of air. The samples were prepared with conventional lockstitch sewing and a newly developed innovative sewing method “Spacer Stitching.” The results are compared with each other in terms of the amount of air present in a unit stitch length.

Calculations based on X-ray tomography images of lockstitch and spacer stitch revealed that, in the case of lockstitch, a unit stitch has a 15% of its volume made up of material and 85% of its volume by air. In comparison, the spacer stitch with the same sewing and fabric parameters has a material volume of 4.6 % and an air volume of 95.4% in a single stitch.

The research can positively improve the thermal properties of sewn material made for insulating purposes of conventional clothing as well as of industrial insulations.

There is no literature available which investigates and calculates the amount of air and material present along with a stitch line.

A method for predicting the material consumption of a sweater is presented before it is knitted. It can be achieved with the five basic models combined with the parameters related to the dimensions of the knitting machine and needles. The paper aims to discuss these issues.

Based on the parameters of the needle bar flat knitting machine, the sweater is modeled with five basic structures. The mathematical expression of each basic structure can be derived with corresponding parameters under some consumptions. In following, the predictive weight of the sweater can be formulated with the expression of the length of the basic structures and the linear density of the yarn.

To evaluate the performance of the proposed scheme, experiments of three types of sweaters on four different knitting machines are carried out. The results show that the proposed method can achieve the performance with the bias values by percentage ranging from −1.54 to −2.84 percent.

Due to the present limited research, more experiments could not be carried out. To improve the performance and robustness of the proposed method, statistical performance measures such as the statistical mean and variance in massive experiments will be studied in the further research.

The evaluation of the material consumption can be obtained before it is knitted with the known basic parameters related to the machine and yarn.

This paper derives the general expressions of five basic structures based on the corresponding parameters of knitting machine. The predictive weight of the sweater is expressed according to the above basic structures before the sweater is knitted.

This paper aims to propose the artificial neural network (ANN) and regression models for the estimation of the thread consumption at multilayered seam assembly stitched with lock stitch 301.

In the present study, the generalized regression and neural network models are developed by considering the fabric types: woven, nonwoven and multilayer combination thereof, with basic sewing parameters: sewing thread linear density, stitch density, needle count and fabric assembly thickness. The network with feed-forward backpropagation is considered to build the ANN, and the training function trainlm of MATLAB software is used to adjust weight and basic values according to the optimization of Levenberg Marquardt. The performance of networks measured in terms of the mean squared error and the layer output is set according to the sigmoid transfer function.

The proposed ANN and regression model are able to predict the thread consumption with more accuracy for multilayered seam assembly. The predictability of thread consumption from available geometrical models, regression models and industrial empirical techniques are compared with proposed linear regression, quadratic regression and neural network models. The proposed quadratic regression model showed a good correlation with practical thread consumption value and more accuracy in prediction with an overall 4.3% error, as compared to other techniques for given multilayer substrates. Further, the developed ANN network showed good accuracy in the prediction of thread consumption.

The estimation of thread consumed while stitching is the prerequisite of the garment industry for inventory management especially with the introduction of the costly high-performance sewing thread. In practice, different types of fabrics are stitched at multilayer combinations at different locations of the stitched product. The ANN and regression models are developed for multilayered seam assembly of woven and nonwoven fabric blend composition for better prediction of thread consumption.

The purpose of this review paper is to define the dominating factors (such as fiber, yarn, fabric structure, sewing thread, sewing needle and machine parameters) that affect the seam damages and causing defects. It also describes the various explanations of sewing defects in garment production and critically analyzes them for optimum selection of parameters and speeds for minimizing such faults. Hence, the knowledge of various factors which affect the sewing damages/defects will be helpful for garment manufacturers/researchers to know influence of the parameters and control the quality of producing seam.

This section is not applicable for a review paper.

Sewing damages such as needle cut and other sewing damages/defects are studied mostly in woven fabric. There are very few studies conducted on knitted fabric sewing damages/defects. The sewing damage problems do not have single solution that is capable of removing these damages in fabric. All the determined and affecting parameters related to fiber, yarn, fabric construction, sewing thread and sewing machine must be examined to design appropriate remedial measurement related to machine design, fabric parameters and sewing thread. This could help in minimizing or eliminating the needle cut and other sewing damage problems.

It is an original review work and is helpful for garment manufacturers/researchers to reduce the defects and be able to produce good quality seam.

Pilling and abrasion resistance are two of the most important mechanical properties of the fabric that influence the appearance and performance of the fabric, particularly in the case of knitted fabrics. Since, these fabric features are affected by fabric structure the aim of present research is to investigate how utilizing miss stitches and tuck stitches in the fabric structure for design purposes will influence the pilling and abrasion resistance of interlock weft-knitted fabrics.

In this research, interlock fabrics with different number of miss or tuck stitches on successive Wales were produced and pilling performance and abrasion resistance of the fabrics were investigated.

The results revealed that increasing the number of miss/tuck stitches on successive Wales decreases the abrasion resistance and enhances the pilling tendency of the fabric. The presence of miss/tuck stitches on both sides of the fabric improves the abrasion resistance and pilling performance of the fabric compared to fabrics containing these stitches on one side of the fabric. Furthermore, the fabric resistance against abrasion and pilling is higher in fabrics consisting of miss stitches compared to fabrics consisting of tuck stitches.

The use of tuck and miss stitches in designing the weft-knitted fabrics is a common method for producing fabrics with variety of knit patterns. Since pilling and abrasion resistance of the fabric influence on its appearance and performance, and none of the previous research studied the pilling and abrasion resistance of interlock-knitted fabrics from the point of presence of tuck and miss stitches on successive Wales of the fabric, this subject has been surveyed in the present research.

The purpose of this paper is to investigate the influence of embroidering technological parameters, of knitted material structure and of either using or not nonwoven material for backing on the quality of restangular embroidered element; based on the obtained results to select the optimal technological parameters for the embroidering of original clothing element avoiding the higher time expenses for the technical development process of new product.

The new methodology for the optimization of technology of original embroidered clothing element is based on the measurements of simple geometric element.

The methodology of technology optimisation based on the measurement of restangular element geometric parameters can also be applied for the optimization of the embroidering technology of advanced design elements.

The present study was carried out investigating knitted materials, but its methodology may be used for woven fabrics also, as their elongation rate is lower than one of knitted materials.

Development of the embroidering technology of original clothing element is based on the scientific approach and industrial experience.

The article presents the test results concerning the effect of certain of the fabric properties and the stitch length on seam puckering when some degree of overfeeding is involved. The results confirm the importance of the dimensionless groups derived in Part 1 of this article.

This paper aims to provide a rapid and accurate method to predict the amount of sewing thread required to make up a garment.

Three modeling methodologies are analyzed in this paper: theoretical model, linear regression model and artificial neural network model. The predictive power of each model is evaluated by comparing the estimated thread consumption with the actual values measured after the unstitching of the garment with regression coefficient R² and the root mean square error.

Both the regression analysis and neural network can predict the quantity of yarn required to sew a garment. The obtained results reveal that the neural network gives the best accurate prediction.

This study is interesting for industrial application, where samples are taken for different fabrics and garments, thus a large body of data is available.

The paper has practical implications in the clothing and other textile‐making‐up industry. Unused stocks can be reduced and stock rupture avoided.

The results can be used by industry to predict the amount of yarn required to sew a garment, and hence enable a reliable estimation of the garment cost and raw material required.