Designing and Development of Denim Fabrics: Part 1 - Study the Effect of Fabric Parameters on the Fabric Characteristics for Women’s Wear

Kumar, Sanjay; Chatterjee, K N

doi:10.4172/2165-8064.1000265

Cited by 6 publications

(4 citation statements)

References 25 publications

(17 reference statements)

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“…One well documented case is the effect of elastane in different fabric families. When applied to denim (i.e., twill) fabrics, elastane is typically introduced in the weft yarns, making the fabric stretchier in that direction [KCPN16, Ery19, EGBDC09]. When applied to jersey knits, the effect is notable in the warp direction and, since the weft stretch is dominated by the straightening of the knitted yarns, the effect of elastane in weft stretch can be less significant [Elt16].…”

Section: Model Inspectionmentioning

confidence: 99%

“…As an illustrative example, the straight yarn arrangement in woven fabrics leads to very stiff stretch properties in the warp and weft directions, while the curved yarn loops present in knitted fabrics allow for additional stretching as the yarns become straightened [GO18]. Many other relations have been documented, such as the influence of different woven and knitted families on the stretch and bending properties of the fabric [BM22,AG20,SA*17,CA00] or the effect of different yarn materials on the resulting fabric mechanical properties [Elt16,HC98,SEHEY12,MEA12,KCPN16, Ery19,EGBDC09].…”

Section: Introductionmentioning

confidence: 99%

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Practical Methods to Estimate Fabric Mechanics from Metadata

Dominguez‐Elvira,

Nicas,

Cirio

et al. 2024

Computer Graphics Forum

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Estimating fabric mechanical properties is crucial to create realistic digital twins. Existing methods typically require testing physical fabric samples with expensive devices or cumbersome capture setups. In this work, we propose a method to estimate fabric mechanics just from known manufacturer metadata such as the fabric family, the density, the composition, and the thickness. Further, to alleviate the need to know the fabric family –which might be ambiguous or unknown for nonspecialists–we propose an end‐to‐end neural method that works with planar images of the textile as input. We evaluate our methods using extensive tests that include the industry standard Cusick and demonstrate that both of them produce drapes that strongly correlate with the ground truth estimates provided by lab equipment. Our method is the first to propose such a simple capture method for mechanical properties outperforming other methods that require testing the fabric in specific setups.

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Section: Model Inspectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Practical Methods to Estimate Fabric Mechanics from Metadata

Dominguez‐Elvira,

Nicas,

Cirio

et al. 2024

Computer Graphics Forum

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“…In fact, the increase in fabric thickness increases thread consumption for the five studied lockstitch groups. As the fabric thickness increased, fabric compressibility, compression recovery, and flexural rigidity increased [11]. Although there is a relationship between fabric compressibility of denim samples and the fabric thickness, the thickness has not been considered in the developed geometrical model due to the thickness measurements have been done after the assembly taking into account the different tension and pressure applied during the sewing process.…”

Section: Fabric Thickness Effectmentioning

confidence: 99%

“…Jaouachi et al provided a rapid and accurate method to predict the amount of sewing thread required to stitch a specific length of woven fabric using lockstitch (301) and chain stitch (401) [9]. Hence, to attempt his goal, the effect of stitches/cm, number of assembly layers and fabric thickness, the fabric mass parameter and the fabric composition, the sewing machine type, the sewing thread count, and the size of needle (fineness or needle count) on the sewing thread consumption values has been studied [10,9,11]. In this context, some studies have been carried out.…”

Section: Introductionmentioning

confidence: 99%

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

Malek

Jaouachi

Khedher

et al. 2020

Autex Research Journal

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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.

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