Investigating Garment Drape Behaviour

Sanad, Reham; Cassidy, Tony

doi:10.3993/jfbi03201505

Cited by 8 publications

(4 citation statements)

References 12 publications

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Section: Methodsmentioning

confidence: 99%

“…For decades, the KES-F systems have been widely used for fabric quality control and selection, textile product development, and the study of fabric properties by various institutions and companies in many countries. [38][39][40] Although the KES offers the possibility of examining 18 parameters concerning fabrics' physical properties, after discussing with experts in textile testing and textile product design, and referring to previous relative literatures on investigating relations between fabric behavior and its physical properties, [3][4][5] in the current study, it is assumed that at most 10 parameters covering fabrics' tensile, shearing, and bending properties are more related to the shaping behavior of a textile material. The surface properties are assumed to be less important.…”

Section: Fabric Objective Measurementmentioning

confidence: 99%

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An intelligent method for the evaluation and prediction of fabric formability for men’s suits

Xue

Zeng

Koehl

2016

Textile Research Journal

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Sixty-six commonly used suitings were selected as the experimental samples of the current study. The Kawabata Evaluation System was used to measure the mechanical properties of the samples. Each sample fabric was made into a shoulder-back as a part of a men’s suit. In order to study the appropriateness of the samples for making good shaped men’s suits, which is known as fabric formability, sensory evaluation methods have been applied to obtain panelists’ assessments on the shape of the shoulder-backs. During data analysis, principal component analysis was initially adopted to reduce the complexity of the system by extracting a small number of important mechanical properties. Then, a fuzzy neural network was developed to model the underlying relations between the samples’ formability and their mechanical properties. Finally, a number of testing samples were used to verify the effectiveness of the proposed predictive model.

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Section: Methodsmentioning

confidence: 99%

Section: Fabric Objective Measurementmentioning

confidence: 99%

An intelligent method for the evaluation and prediction of fabric formability for men’s suits

Xue

Zeng

Koehl

2016

Textile Research Journal

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“…For various analysis methods for Cusick's drape, we refer to [9]. Numerous studies have demonstrated that Cusick's drape is highly correlated with most KES measurements [10]- [13]. For example, Lam et al [14] proposed a method for estimating the Cusick's drape coefficient from mechanical properties of fabric using neural network learning.…”

Section: A Hanging Drape Vs Cusick's Drapementioning

confidence: 99%

Estimating Cloth Simulation Parameters From a Static Drape Using Neural Networks

Ju¹,

Choi

2020

IEEE Access

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We present a neural network learning approach for estimating a set of cloth simulation parameters from a static drape of a given fabric. We use a variant of Cusick's drape, which is used in the fashion textile industry to classify fabric according to mechanical properties. In order to produce a large enough set of reliable training data, we first randomly sample simulation parameters using a Gaussian mixture model that is fitted with 400 sets of primary simulation parameters derived from real fabrics. Then, we simulate our modified Cusick's drape for each sample parameter set. To learn the training data, we propose a two-stream fully connected neural network model. We prove the suitability of our neural network model through comparisons of the learning errors and accuracy with other similar neural network and linear regression models. Additionally, to demonstrate the practicality of our method, we reproduce the drape shapes of real fabrics using the simulation parameters estimated from the trained neural networks.

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“…Image analysis was then carried out on the images captured by camera from below the garments suspended on a mannequin. They proved conclusively that there was a very poor correlation between fabric and garment drape values.They went on to derive an equation to predict drape rank scores for garments depending on circularity and wavelength minimum [121][122][123]. Importantly existing tests of drape use templates which do not represent cross section of the body at even key locations.…”

Section: Fabric Drape Versus Garment Drapementioning

confidence: 99%