Predicting Sewing Thread Consumption for Chainstitch Using Regression Model

Sharma, Suraj; Gupta, Manoj; Vk, Midha

doi:10.4172/2165-8064.1000295

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…Các nhà nghiên cứu đã phát triển các mô hình để dự báo mức tiêu hao chỉ may của các lớp đường may khác nhau bằng cách sử dụng các kỹ thuật như lôgic mờ, phân tích hồi quy, mạng nơ ron nhân tạo, và mô hình hóa [1,11,12,13,14,17,19,21,23]. Một vài nghiên cứu thiết lập mô hình tính tiêu hao chỉ [7], các yếu tố ảnh hưởng [8], lượng tiêu hao chỉ thoi [9] của đường may 301 và 401 [10].…”

Section: đặT Vấn đềunclassified

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Nghiên cứu định mức chỉ trong may công nghiệp

2023

jsthaui

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Định mức chỉ trong may công nghiệp là cơ sở quan trọng để tính toán giá thành sản phẩm, chuẩn bị đúng và đủ chỉ may cho sản xuất và góp phần tìm ra biện pháp tiết kiệm chỉ may trong quá trình xây dựng mức. Bài báo này trình bày kết quả nghiên cứu xác định định mức chỉ cho sản phẩm may và đơn hàng trong may công nghiệp dựa trên các mô hình tính lượng chỉ tiêu hao cho đường may thông dụng xây dựng từ dữ liệu thực nghiệm ứng dụng kỹ thuật BMA (Bayesian Model Average). Chương trình phần mềm TC.V1.0 đã được thiết lập để hỗ trợ quá trình tính định mức chỉ may cho sản phẩm và đơn hàng. 24 mô hình tính lượng chỉ tiêu hao cho 24 loại đường may đã được xác định với hệ số R 2 từ 0,964 đến 0,999.Từ khóa: Tiêu hao chỉ; đường may thông dụng; kỹ thuật BMA.

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Section: đặT Vấn đềunclassified

“…Sharma và cộng sự phát triển mô hình dự đoán mức tiêu hao chỉ bông và chỉ lõi polyester -bông. Hệ số xác định trong mối tương quan giữa kết quả dự báo và thực tế đạt được là 0,956 [21]. M. [20].…”

Section: đặT Vấn đềunclassified

Nghiên cứu định mức chỉ trong may công nghiệp

2023

jsthaui

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“…This consumption is estimated for heavy fabrics that are thicker than the average fabrics [24]. However, Sharma proved that the material thickness has the most important infl uence on seam consumption because in his case, the material thickness has a very large interval (from1.64 to 6.56 mm) [9].…”

Section: Materials Thickness Effectmentioning

confidence: 99%

“…Moreover, this result is in good agreement with the fi ndings of Lauriol's study [4] which proves that if the sewing length decreases from 2.5 mm to 2 mm (from 4 stitches/cm to 5 stitches/cm), thread consumption increases, approximately, by 10%. According to Sharma study, the stitch density is the second most infl uential parameter on seam thread consumption because, in his case, the stitch density has a small interval (from 2 to 3.5 stitches/cm) [9]. Besides, Abhar calculated the contribution ratio percentage for stitch class 504 and concluded that the contribution ratio of stitch density was around 62% [11].…”

Section: Stitch Density Effectmentioning

confidence: 99%

“…But, Jaouachi provided an accurate method to predict the amount of sewing thread required to stitch woven fabric using lockstitch (301) and chain stitch (401) using different methods, which are regression models, neural regression, geometrical model and fuzzy method [1,7,8]. Sharma and Vlinay calculated the sewing thread consumption for chain stitch and lockstitch, respectively, using regression model [9,10]. Abher has used a geometrical model to determine the consumption of sewing thread for 504 over-edge stitch [11].…”

Section: Introductionmentioning

confidence: 99%

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Determination of Sewing Thread Consumption for 602, 605, and 607 Cover Stitches Using Geometrical and Multi-Linear Regression Models

Malek

Jaouachi

Dominique

2020

Autex Research Journal

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This work deals with prediction of the quantities of sewing threads required to sew a garment using cover stitches for the different classes of 600 (602, 605 and 607 cover stitches) by performing a rapid and precise methods (Geometrical and statistical). Sewing consumption value was estimated based on the geometrical method of different cover stitch shapes to prevent inventory of stocks for each stitch type. In the prediction of the sewing thread consumption for each investigated stitch, it was assumed that the consumption is a function of the input parameters, such as material thickness, stitch density, yarn linear density, and seam width. In addition, a statistical method based on multi-linear regression was established. The coefficient R2 value was determined to evaluate the precision of the geometrical and statistical methods. By comparing the theoretical thread consumption with the experimental ones, it is concluded that the geometrical method is more accurate than the statistical method regarding the range of R2 (from 98.78% to 99.38%), which encouraged industrialists to use geometrical models to predict thread consumption.The input parameters contributing to the sewing thread consumption behavior were studied and analyzed. It was concluded that the most important parameters influencing thread consumption are stitch density followed by seam width. Both yarn density and material thickness have a low contribution to the thread consumption value.

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Development of prediction model through linear multiple regression for the prediction of longitudinal stiffness of embroidered fabric

Dutta

Chatterjee

2020

Fash Text

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Embroidery through computer aided semi-automatic machines is one of the most widely used option for the surface ornamentation of apparel fabrics at present. Since the embroidery process includes addition of certain amount of embroidery-threads depending upon the design motif, it is quite obvious that basic physical and functional properties of fabric are subject to change. It is therefore important to develop an algorithm or empirical equation for proper prediction of the properties of the embroidered fabric, relevant to its required end-use in apparel industry. In this context, an effort has been made to determine a prediction equation through linear multiple regressions for the prediction of longitudinal stiffness of embroidered fabric in terms of flexural rigidity in warp direction of the base fabric, considering the input parameters as warp-way flexural rigidity of the base fabric, breaking load and linear density of the embroidery thread, stitch density, average stitch length and average stitch angle of the embroidery design. The final Prediction model is statistically verified taking new embroidery samples of different varieties. It is found that the model can predict with a very satisfactory level of accuracy. Also, the influences of the embroidery parameters in this context have been analyzed through the corresponding regression coefficients and the three dimensional (3D) surface curves. Stitch density has been emerged as the most influential parameter, followed by the stitch length and the stitch angle.

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Predicting Sewing Thread Consumption for Chainstitch Using Regression Model

Cited by 5 publications

References 6 publications

Nghiên cứu định mức chỉ trong may công nghiệp

Nghiên cứu định mức chỉ trong may công nghiệp

Determination of Sewing Thread Consumption for 602, 605, and 607 Cover Stitches Using Geometrical and Multi-Linear Regression Models

Development of prediction model through linear multiple regression for the prediction of longitudinal stiffness of embroidered fabric

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