Modelling of thermal conductivity of knitted fabrics made of cotton–bamboo yarns using artificial neural network

Majumdar, Abhijit

doi:10.1080/00405000.2010.516929

Cited by 41 publications

(14 citation statements)

References 14 publications

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“…If the fabric is thick and rough enclosing lots of air, the conductivity is much lower. Majumdar [44] showed that thermal conductivity decreases with increase in fabric thickness. Moreover, research has shown that knitted fabrics have lower values of thermal conductivity in comparison with woven fabrics [2] because they are generally thicker than that of woven fabrics.…”

Section: Fabric Construction and Materialsmentioning

confidence: 99%

ThermalWear: Exploring Wearable On-chest Thermal Displays to Augment Voice Messages with Affect

Ali

Yang

Ananthanarayan

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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Voice is a rich modality for conveying emotions, however emotional prosody production can be situationally or medically impaired. Since thermal displays have been shown to evoke emotions, we explore how thermal stimulation can augment perception of neutrally-spoken voice messages with affect. We designed ThermalWear, a wearable on-chest thermal display, then tested in a controlled study (N=12) the effects of fabric, thermal intensity, and direction of change. Thereafter, we synthesized 12 neutrally-spoken voice messages, validated (N=7) them, then tested (N=12) if thermal stimuli can augment their perception with affect. We found warm and cool stimuli (a) can be perceived on the chest, and quickly without fabric (4.7-5s) (b) do not incur discomfort (c) generally increase arousal of voice messages and (d) increase / decrease message valence, respectively. We discuss how thermal displays can augment voice perception, which can enhance voice assistants and support individuals with emotional prosody impairments.

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Section: Fabric Construction and Materialsmentioning

confidence: 99%

ThermalWear: Exploring Wearable On-chest Thermal Displays to Augment Voice Messages with Affect

Ali

Yang

Ananthanarayan

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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“…Artificial Neural Networks (ANNs) has been extensively used at a research stage in several fields of textile industry since last decades [45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61]. ANNs are particularly applied in spinning, weaving, dyeing and quality control engineering.…”

Section: Artificial Neural Network-based Approachesmentioning

confidence: 99%

Machine Vision-Based Pilling Assessment: A Review

Furferi

Governi

Volpe

2015

Journal of Engineered Fibers and Fabrics

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Pilling is an undesired defect of textile fabrics, consisting of a surface characterized by a number of roughly spherical masses made of entangled fibers. Mainly caused by the abrasion of fabric surface occurring during washing and wearing of fabrics, this defect needs to be accurately controlled and measured by companies working in the textile industry. Pilling measurement is traditionally performed using manual procedures involving visual control of fabric surface by human experts. Since the early nineties, great efforts in developing automatic and non-intrusive methods for pilling measurement have been made all around the world with the final aim of overcoming traditional, visual-based and subjective procedures. Machine Vision proved to be among the best options to perform such defect assessment since it provided increasingly performing measurement equipment and tools, serving the purpose of automatic control. In particular, a relevant number of interesting works have been proposed so far, sharing the idea of helping (or even replacing) traditional measurement methods using image processing-based ones. The present work provides a rational and chronological review of the most relevant methods for pilling measurement proposed so far. This work serves the purposes of 1) understanding whether today's automatic machine vision-based pilling measurement techniques are ready for supplanting traditional pilling measurement and 2) providing textile technology researchers with a bird's eye view of the main methods studied to confront with this problem.

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“…The comfort properties of raw fabrics, produced from pure cotton or regenerated cellulosic fibres or the blends of these materials, in knitted or woven structures, have been investigated by several researchers. [2][3][4][5][6][7][8] The linear density of yarn, fabric thickness, fibre content and knit structures have been found to be important parameters, affecting comfort properties. In order to simulate real use conditions, some of the properties of the fabrics have been evaluated after repeated launderings by several researchers.…”

Section: Introductionmentioning

confidence: 99%

Effects of Repeated Laundering on Thermal Comfort Properties of Cellulosic Knitted Fabrics

Demiryürek¹,

Güleyüpoğlu²,

Turhan³

2019

Cellulose Chem. Technol.

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Air and water vapour permeability, thickness and thermal resistance of fabrics are some of the most important parameters to determine clothing comfort. However, these parameters tend to change after laundering in daily use. In this study, changes in the thermal comfort properties of cellulosic fabrics used in underwear and sock production, due to repeated laundering, were investigated. For this purpose, 100% cotton, viscose, Viloft, Modal, Promodal and Tencel fabrics, in single jersey construction, were selected and the thickness, air permeability, water vapour permeability and thermal resistance of the fabrics were measured initially and after 5, 10 and 15 repeated launderings. The obtained data were analysed by statistical techniques. According to the results, increasing the number of laundering cycles decreases air permeability and increases thickness and thermal resistance, in general. However, relative water vapour permeability decreases up to a number of laundering cycles and increases from this point on, upon further increasing the number of laundering cycles. The material type was found to be more influential than thickness on the thermal resistance values in this study. In conclusion, repeated laundering and material type significantly affect the comfort properties of fabrics.

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Modelling of thermal conductivity of knitted fabrics made of cotton–bamboo yarns using artificial neural network

Cited by 41 publications

References 14 publications

ThermalWear: Exploring Wearable On-chest Thermal Displays to Augment Voice Messages with Affect

ThermalWear: Exploring Wearable On-chest Thermal Displays to Augment Voice Messages with Affect

Machine Vision-Based Pilling Assessment: A Review

Effects of Repeated Laundering on Thermal Comfort Properties of Cellulosic Knitted Fabrics

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