A new method for measuring the static and dynamic fabric/garment drape using 3D printed mannequin

Issa, Marwa; Elgholmy, Sherwet; Sheta, Aida M.F.; Fors, M. Nashat

doi:10.1080/00405000.2021.1917803

Cited by 3 publications

(2 citation statements)

References 36 publications

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“…However, there is significant information during the movement of fabrics. The information reflects the fabric shape retention, such as the dynamic fabric drapability [26].…”

Section: Introductionmentioning

confidence: 99%

Characterizing fabric crease recovery through sequential image analysis

Zhang,

Wang,

et al. 2024

Meas. Sci. Technol.

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Crease recovery is the ability of a fabric to revert to its original condition after deformation or folding. This recovery process is intricately linked to several fabric properties, including fiber content, yarn structure, weave, fabric finish, and mechanical treatments. Based on the dynamic nature of crease recovery, this paper employs sequential image analysis to track the velocity of fabric crease recovery at different positions and extract simple metrics for measuring fabric shape retention. In each image, the contour of the creased sample is detected, and the contour is modeled by a Gaussian function to calculate its barycenter. The barycenter of a crease is the point in space where the mass of the crease is concentrated, reflecting the shape and position of the crease. During the recovery process, the translation of the barycenter of the creased sample can be determined from the sequential images, leading to the calculation of crease’s recovery velocity. Experimental results demonstrate a linear relationship between the barycentric velocity and logarithmic time. The slope of the resulting fit line, designated as the crease coefficient k, serves as a singular metric for assessing the fabric’s shape retention following the release of the crease. This methodology is benchmarked against traditional fabric crease behavior tests, including the draping coefficient, bending length, and crease recovery angle. It demonstrates that the crease coefficient k offers greater reliability and accuracy across tests on 10 diverse fabric samples, which varied in terms of fiber content, weave, yarn size, and density.

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“…However, there is significant information during the movement of fabrics. The information reflects the fabric shape retention, such as the dynamic fabric drapability [26].…”

Section: Introductionmentioning

confidence: 99%

Characterizing fabric crease recovery through sequential image analysis

Zhang,

Wang,

et al. 2024

Meas. Sci. Technol.

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“…In the study, different stitch and seam values, bending length, and their effect on the drape properties were evaluated. They concluded that seam allowance increases the bending length, hence reducing the drape coefficient and simple radial stitch increases the drape coefficient Issa et al. (2022) measured fabric drape behaviors using a 3D printed mannequin and stated that stitch types, fabric weight, and bending rigidity are effective on fabric dynamic drape behaviors since they limit fabric movement.…”

Section: Introductionmentioning

confidence: 99%

A research for the effectiveness of stitch type and stitch direction on dynamic drape behaviors

Demir,

Balci Kilic

2023

IJCST

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PurposeThe purpose of this study is to explore the effect of stitch type and stitch direction on the dynamic drape behavior of the woven fabric.Design/methodology/approachIn this paper, the effectiveness of stitch type and stitch directions on dynamic drape behaviors were investigated. Fabric parts were sewn together with two types of the stitch (lockstitch and overlock stitch) on three different stitch directions (warp, weft and bias (45°)). The static drape coefficients (SDC) of unsewn and sewn fabrics were measured according to the image process method. Dynamic drape coefficients (DDC) of fabrics were also measured using the same method at six different (25, 50, 75, 100, 125, 150 rpms) rotation speeds. Additionally, bending length and bending rigidity were measured using the Cantilever test method.FindingsExperimental results showed that stitch type and stitch directions are effective on the dynamic drape behaviors of the fabric. Overlock stitch resulted in greater DDC than the lock stitch. For both of the stitch type, DDC for the stitch on the warp direction are greater than the stitch on the weft and bias direction for all speeds. In addition, bending length, hence the bending rigidity, are greater for overlock stitch type and always weft direction resulted in greater than the warp and bias direction.Originality/valueFabric drape is vital for garment appearance and is gaining popularity with the advancement of virtual technology, enabling virtual visualization of garments. While previous studies have predominantly examined either the static or dynamic drape behavior of individual fabric panels, or solely focused on the static drape behavior of sewn fabrics, this study acknowledges the significance of incorporating the influence of stitch type and direction on dynamic drape behaviors. Considering that fabrics are sewn together to create garments and that DDC provides a more accurate representation of real-time fabric behavior compared to SDC, this research makes a valuable contribution to the existing literature by investigating the impact of stitch type and direction specifically on DDC.

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A Systematic Review of AI-Driven Prediction of Fabric Properties and Handfeel

Tu,

Kwan,

Yick

2024

Materials

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Artificial intelligence (AI) is revolutionizing the textile industry by improving the prediction of fabric properties and handfeel, which are essential for assessing textile quality and performance. However, the practical application and translation of AI-predicted results into real-world textile production remain unclear, posing challenges for widespread adoption. This paper systematically reviews AI-driven techniques for predicting these characteristics by focusing on model mechanisms, dataset diversity, and prediction accuracy. Among 899 papers initially identified, 39 were selected for in-depth analysis through both bibliometric and content analysis. The review categorizes and evaluates various AI approaches, including machine learning, deep learning, and hybrid models, across different types of fabric. Despite significant advances, challenges remain, such as ensuring model generalization and managing complex fabric behavior. Future research should focus on developing more robust models, integrating sustainability, and refining feature extraction techniques. This review highlights the critical gaps in the literature and provides practical insights to enhance AI-driven prediction of fabric properties, thus guiding future textile innovations.

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A new method for measuring the static and dynamic fabric/garment drape using 3D printed mannequin

Cited by 3 publications

References 36 publications

Characterizing fabric crease recovery through sequential image analysis

Characterizing fabric crease recovery through sequential image analysis

A research for the effectiveness of stitch type and stitch direction on dynamic drape behaviors

A Systematic Review of AI-Driven Prediction of Fabric Properties and Handfeel

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