Determination of optimal system parameters to characterize the wrinkle recovery of fabrics by an integrated shape retention evaluation system

Lu, Yuzheng; Hu, Xiaojun; Sun, Fengxin; Peng, Fujian; Gao, Weidong

doi:10.1177/0040517519858770

Cited by 8 publications

(4 citation statements)

References 25 publications

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“…In addition, there is a series of methods that does not measure the wrinkles on the sample surface, namely indirect measurement. These methods evaluate the fabric wrinkle property by measuring the fabric weave parameters, mechanical properties [49], in-situ characterization [50], and shape retention properties [51] of the fabric sample and mapping to the smoothness appearance degree or wrinkle recovery angle.…”

Section: F Objective Methods For Fabric Wrinkle Evaluationmentioning

confidence: 99%

Fusing Convolutional Neural Network Features With Hand-Crafted Features for Objective Fabric Smoothness Appearance Assessment

et al. 2020

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In the textile and apparel industry, it remains a challenging task to evaluate the fabric smoothness appearance objectively. In existing studies, with computer vision technology, researchers use the hand-crafted image features and deep convolutional neural network (CNN) based image features to describe the fabric smoothness appearance. This paper presents an image classification framework to evaluate the fabric smoothness appearance degree. The framework contains a feature fusion module to fuse the handcrafted and CNN features to take both advantages of them. The framework uses the multi-scale spatial masking model and a pre-trained CNN to extract hand-crafted and CNN features of fabric images respectively. In addition, a mislabeled sample filtering module is set in the framework, which helps to avoid the negative impact of mislabeled samples in training. In the experiments, the proposed framework achieves 85.2%, 96.1%, and 100% average evaluation accuracies under errors of 0 degree, 0.5 degree, and 1 degree respectively. The experiments on the feature fusion and mislabeled sample filtering verified their effectiveness in improving the evaluation accuracies and the label noise robustness. The proposed method outperforms the state-of-theart methods for fabric smoothness assessment. Promisingly, this paper can provide novel research ideas for image-based fabric smoothness assessment and other similar tasks.

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Section: F Objective Methods For Fabric Wrinkle Evaluationmentioning

confidence: 99%

Fusing Convolutional Neural Network Features With Hand-Crafted Features for Objective Fabric Smoothness Appearance Assessment

et al. 2020

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“…The weight was calculated by testing the mass of per unit area of 100 cm 2 circular fabric samples by an electro balance with a precision scale of ±0.1 mg. The fabrics were cut into width 3 length of 3 cm 3 5 cm for instrumental measurement and this sample size was optimized according to our previous study (Lu et al, 2020) by simultaneously considering the instrumental sensitivity and measurement accuracy. Rectangular fabric samples in size of 10 cm 3 10 cm were also prepared for subjective evaluation.…”

Section: Curve Analysis and Index Definitionmentioning

confidence: 99%

Digitization of fabric comfort: a multidimensional evaluation strategy to human perceptions of sensorial, thermal and acoustic comfort in clothing

Chen²,

Sun³

2022

IJCST

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PurposeCurrent measurement methods for fabric comfort attributes generally suffer from either complicated testing processes and intricate measuring equipment or partial evaluation objectives and thus are difficult for effectively evaluating multidimensional human perceptions towards the comprehensive comfort of fabrics. The purpose of this paper is to develop a facile test device, namely fabric comfort tester, to achieve a comprehensive evaluation of human sensations in terms of sensorial, thermal and acoustic comfort in clothing.Design/methodology/approachThe prototype of the designed device was introduced, which enables a simultaneous test for multiple physical and mechanical properties of fabrics based on a force sensor and a set of infrared sensors via constructing multi-deformation states of the measured fabrics. Eleven measurement indices extracted from the measurement curves are defined and interpreted based on correlation analysis. A series of regression models are developed by relating the measurement indices with subjective evaluation results and validated by a set of independent samples.FindingsHuman perceptions of sensorial, thermal and acoustic comfort in clothing can be predicted by the measured physical indices and the designed test device with the developed regression models provides an alternative method to characterize the fabric comfort attributes effectively.Originality/valueThe work develops a novel device for objective evaluation of fabric comfort properties by a simultaneous test, integrating the mechanical measurement with thermal test and thereby filling the gap between the existing evaluation methods and practical requirements for the digitalization of fabric comfort in present textile and garment trade.

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“…In the testing process, the parameters of the FMDT were set as follows 35 : the horizontal distance between the two jaws was set as 10 mm; the maximum compression force of the compression step and maximum tensile force of the stretching step were both set as 300 cN, and the hold time of the maximum forces was set as 30 s. The relative moving speed of the moving plate to the measuring plate was 25 mm/min, and the sampling frequency was set as 100 Hz. Three measurements were conducted for each fabric sample, obtaining three force-displacement curves, and the average of the three curves was reported.…”

Section: The Fmdt Testingmentioning

confidence: 99%

In situ characterization of the morphological wrinkling of woven fibrous materials by a mechanical test

Sun

Wang

et al. 2020

Textile Research Journal

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Wrinkling is one of the most common flaws of woven fabrics in domestic use and industrial applications. It is necessary to develop an objective evaluation method to quantify the smoothness appearance of fabrics effectively. Herein, a fabric multi-deformation tester (FMDT) was designed to evaluate the smoothness appearance of garment fabrics by one sequential mechanical test, overcoming the main difficulties of the existing visual measurement methods for wrinkling evaluation of fabrics with complex colors and patterns. The k-means clustering algorithm was used to objectively cluster the fabric samples based on the characteristic parameters, including the wrinkle-induced residual force ( F wr), hysteresis distance ( H fr), position deflection ( D fr) and stretching recovery slope ( S tr), from the testing curve and the thickness and weight of fabrics, and comparisons with subjective evaluation were also conducted. The results reveal that the k-means clustering is able to classify the smoothness appearance of fabrics using the selected characteristic parameters, showing a good consistency with the subjective clustering results. The feasibility of using the mechanical and deformation properties of textiles to characterize fabric smoothness appearance is proved, and the FMDT provides a potential method to analyze the wrinkling of fibrous materials in a convenient way.

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Determination of optimal system parameters to characterize the wrinkle recovery of fabrics by an integrated shape retention evaluation system

Cited by 8 publications

References 25 publications

Fusing Convolutional Neural Network Features With Hand-Crafted Features for Objective Fabric Smoothness Appearance Assessment

Fusing Convolutional Neural Network Features With Hand-Crafted Features for Objective Fabric Smoothness Appearance Assessment

Digitization of fabric comfort: a multidimensional evaluation strategy to human perceptions of sensorial, thermal and acoustic comfort in clothing

In situ characterization of the morphological wrinkling of woven fibrous materials by a mechanical test

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