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1

Sewing Thread Consumption for Chain Stitches of Class 400 using Geometrical and Multilinear Regression Models

Malek Sarah, Khedher Faouzi, Adolphe Dominique C., Jaouachi Boubaker

Autex Research Journal

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2021

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Vol. 21, no. 1

52--62

EN

This work deals with determination of rapid and precise methods to predict the amount of sewing thread needed to sew a garment using different chain stitches of the class 400 (from 401 to 407 chain stitches). At first, to avoid unused stocks, sewing consumption value was determined using a geometrical method (based on different chain stitch shapes). The prediction of the sewing thread consumption was proposed as a function of the studied input parameters, which are fabric thickness, stitch density, yarn linear density, and stitch width. Then, a statistical method based on the multilinear regression was studied. Geometrical and statistical results were discussed. Based on the R2 range, we concluded that the geometrical method is more accurate than the statistical one (from 98.16 to 99.19% and from 97.30 to 98.51%, respectively). Thus, this result encourages industrialists to use geometrical models to predict thread consumption. Also, all studied parameters, contributing to the sewing thread consumption behavior, were investigated and analyzed. The result shows that the most important parameters affecting thread consumption are stitch density followed by stitch width and fabric thickness. The yarn density has a low contribution on the thread consumption value.

2

Prediction of Sewing Thread Consumption for Over-Edge Stitches Class 500 Using Geometrical and Multi-Linear Regression Models

Malek Sarah, Adolphe Dominique C., Jaouachi Boubaker

Autex Research Journal

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2021

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Vol. 21, no. 2

150--162

EN

Rapid and precise methods (geometrical and statistical), which aim to predict the amount of sewing thread needed to sew a garment using different over-edge stitches of class 500 (501, 503, 504, 505, 512, 514, 515, and 516), have been provided. Using a geometrical method of different over-edge stitch shapes, sewing consumption value was determined to avoid the unused stocks for each stitch type. The prediction of the sewing thread consumption relative to each investigated over-edge stitch was proposed as a function of the studied input parameters, such as material thickness, stitch density, yarn diameter, and seam width (distance between the needle and the cutter and the distance between two needles). To improve the established models using a geometrical method, a statistical method based on multi-linear regression was studied. Geometrical and statistical results were discussed, and the coefficient R2 value was determined to evaluate the accuracy of the tested methods. By comparing the estimated thread consumption with the experimental ones, we concluded that the geometrical method is more accurate than the statistical method regarding the range of R2 (from 97.00 to 98.78%), which encourages industrialists to use geometrical models to predict thread consumption. All studied parameters contributing to the sewing thread consumption behavior were investigated and analyzed in the experimental design of interest. It was concluded that the most important parameter affecting thread consumption is the stitch density. The material thickness and the seam width (B1) have a little impact on thread consumption values. However, the seam thread diameter has a neglected effect on thread consumption.

3

Sewing Thread Consumption for Different Lockstitches of Class 300 Using Geometrical and Multi-Linear Regression Models

Malek Sarah, Jaouachi Boubaker, Khedher Faouzi, Adolphe Dominique

Autex Research Journal

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2020

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Vol. 20, no. 4

415--425

EN

This paper aims to provide rapid and precise methods to allow industrials to predict the amount of sewing thread needed to sew a garment using different lockstitches of class 300 (301, 301/301, 304, 308, 309, 310, 311, 312, and 315). To avoid unused stocks for each stitch type, a sewing consumption value was determined using a geometrical method of different lockstitch shapes. Furthermore, the relationships between overall geometrical models of the studied lockstitches of class 300 were developed. Indeed, based on the geometrical model of lockstitch type 301, all theoretical models proposed were investigated and proved to be accurate. Moreover, referring to the findings, the prediction of the sewing thread consumption relative to each investigated lockstitch was proposed as a function of the studied input parameters. To improve the established models using geometrical technique, a statistical method was conducted. In addition, based on multi-linear regression, compared geometrical and statistical results were discussed and the coefficient R2 value was determined to evaluate the accuracy of the tested methods. By comparing the estimated thread consumption with the experimental ones, we concluded that the accuracy of the models is significant (R2 ranged from 93.91% to 99.10%), which encourages industrialists to use geometrical models to predict thread consumption. Therefore, the accuracy of prediction using the geometrical method is more accurate than the statistical method regarding the range of R2 (from 92.84% to 97.87%). To classify the significance of all studied parameters, their contributions to the sewing thread consumption behavior were analyzed in the experimental design of interest. It was concluded that the most important parameters affecting thread consumption are stitch width, stitch density, and the gap between two needles. The thickness of fabric has a low contribution to the thread consumption value, whereas the effect of yarn count can be neglected.

4

Evaluation of Sewed Thread Consumption of Jean Trousers Using Neural Network and Regression Methods

Jaouachi B., Khedher F.

Fibres & Textiles in Eastern Europe

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2015

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Vol. 23, no. 3 (111)

91--96

EN

This paper deals with the prediction of the sewing thread consumption of jean trousers using the neural network technique. The neural network results and analysis are discussed and investigated. Indeed the findings show that neural network consumption values give better fitting of experimental results than the ones obtained using regression technique. However, compared to the experimental consumption results, theoretical ones of the sewn jean pants seem widely predictable in the desired field of interest. Among the all parameters studied, statistical analysis results also indicate that five inputs can be considered as influential ones. When classifying these five influential inputs, only three parameters are considered most significant. In fact the thread consumed to sew jean trouser samples remains influenced especially by the thread properties and needle fineness as well. Compared with the regression model, the neural network model gives a more accurate prediction and to a great extent provides the amount of sewing thread.

PL

Praca dotyczy przewidywania zużycia nici szewnych przy szyciu spodni dżinsowych stosując technikę sztucznych sieci neuronowych. Badania wykazują , że wyniki zapotrzebowania nici uzyskane za pomocą sztucznych sieci neuronowych są bardziej zgodne z eksperymentami niż te uzyskane techniką regresji. Przeprowadzając kolejne analizy, określono najbardziej racjonalną strukturę sztucznych sieci neuronowych z pięcioma wejściami i trzema parametrami mającymi najbardziej istotny wpływ na zużycie. Stwierdzono, że zależy ono głównie od właściwości nitki i rodzaju igły. Porównując wyniki otrzymane z zastosowania sztucznych sieci neuronowych z wynikami otrzymanymi za pomocą metody regresji stwierdzono, że pierwsza metoda daje lepsze wyniki.

5

A simple system for the online detection of skip/loop stitches in single needle lockstitch sewing machines

Wesley D. S., Poojitha V.

Autex Research Journal

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2010

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Vol. 10, no. 3

69--72

EN

In single needle lockstitch sewing machines, the needle thread consumption of 10 normal stitches for a fabric with a particular number of layers at a particular stitches per unit length is manually measured kept as a reference value. If the value of needle thread consumed per stitch is very small, the length of thread consumed per 10 stitches is used as a comparing value. The actual thread consumed for every 10 stitches is measured online by a rotary optical encoder sensor by converting the angular movement into the linear movement of the thread and continually compared with the reference value. If the online measured length is more or less a buzzer sounds to indicate the variation. The counting of every stitch formed is undertaken by a proximity sensor by sensing the protrusion in the hand wheel of the machine, which rotates once for every rotation.