Thermal analysis of conventional and performance plain woven fabrics by finite element method

Siddiqui, Muhammad Owais Raza; Sun, Danmei

doi:10.1177/1528083717736104

Cited by 27 publications

(19 citation statements)

References 25 publications

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“…Siddiqui and Sun 14 and Ng et al [15][16][17] proposed a defect learning method based on the convolution neural network model for yarn fabric to process defects occurring during the production process. In this method they used a golden subtraction method for internal decomposed fabric.…”

Section: Literature Reviewmentioning

confidence: 99%

Effective textile quality processing and an accurate inspection system using the advanced deep learning technique

Pitchaimani¹,

Rajan²

2019

Textile Research Journal

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This research paper focuses on the innovative detection of defects in fabric. This approach is based on the design and development of a computer-assisted system using the deep learning technique. The classification network is modeled using the ResNet512-based Convolutional Neural Network to learn the deep features in the presented fabric. Being an accurate method, this enables accurate localization of minute defects too. Our classification is based on three major steps; firstly, an image acquired by the NI Vision model and pre-processed for a standard pattern to Kullback Leibler Divergence calculation. Secondly, standard textile fabrics are presented to train the Convolutional Neural Network to classify the defective region and the defect-free region. Finally, the testing fabrics are examined by the trained deep Convolutional Neural Network algorithm. To verify the performance, multiple fabrics are presented and the classification accuracy is evaluated. For standard defects on defective fabrics, an average accuracy of 96.5% with 98.5% precision is obtained. Experimental results on the standard Textile Texture Database dataset confirmed that our method provides better results compared with similar recent classification methods, such as the Support Vector Machine and Bayesian classifier.

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Section: Literature Reviewmentioning

confidence: 99%

Effective textile quality processing and an accurate inspection system using the advanced deep learning technique

Pitchaimani¹,

Rajan²

2019

Textile Research Journal

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“…Furthermore, in order to determine the accuracy of experimental results, comparisons were made between tested results and results obtained through finite element modeling, shown in Figure 9(a) and (b). Detailed information about finite element simulation can be found in published works by the authors (Siddiqui & Sun, 2013, 2015, 2016, 2017). In the case of thicker fabric, there must be insulation to cover the heat loss from the fabric’s edges; this should be addressed in future work.…”

Section: Validation Of Experimental Setupmentioning

confidence: 99%

Development of Experimental Setup for Measuring the Thermal Conductivity of Textiles

Siddiqui

Sun

2018

Clothing and Textiles Research Journal

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The thermophysical properties of textile materials are very important in helping to understand the thermal comfort of fabrics for clothing and technical textiles. An experimental setup for the measurement of the thermal conductivity of fabrics was developed based on the heat flow meter principle. The setup was considered highly accurate and reliable based on the low absolute error, high correlation coefficient, and the coefficient of determination between the results from the setup and commercially available devices. The setup is easy to use for testing any textile-based materials and their composites.

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“…Gong et al 9 established a one-dimensional heat transfer model of the fabric, and studied the thermal protection performance of the fabric using ANSYS software. Siddiqui and Sun [10][11][12] used FEA method to predict the effective thermal conductivity and thermal resistance of fabrics according to the actual parameters of fabrics. Fan et al 13 proposed a three-layer branched chain structure bionic woven fabric, and analyzed the steady-state heat transfer performance of the fabric by using FEA method.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional analysis model of electric heating fabrics considering the skin metabolism

Xiao

Kuai

et al. 2021

Journal of Engineered Fibers and Fabrics

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In low temperature environment, electric heating clothing can provide extra heat for human body through built-in heat source, so it has better thermal insulation effect. The thermal analysis is the initial step for electric heating clothing design. The current thermal analysis of electric heating textiles focuses on the fabric itself instead of the effect of skin tissue metabolism and heat production. In order to improve the accuracy of skin surface temperature prediction, the biological heat transfer need be modeled to analyze the internal temperature distribution of the heating suit system. In this paper, a three-dimensional (3D) thermal analysis model of electric heating clothing combined with human skin tissue is established. Firstly, the coupling analysis of Fourier heat conduction and Pennes biological heat transfer equation is carried out. Then the reliability of the 3D thermal analysis model is verified by finite element analysis (FEA). The results show that the fitting error between the three-dimensional model analysis data and FEA simulation data is 5°C, which proves that the model can accurately predict the system temperature. Finally, we make further research about the effects of ambient temperature, clothing layer thickness, and input power on the maximum skin surface temperature. This study provides theoretical foundation for the design of wearable thermal management fabric.

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Thermal analysis of conventional and performance plain woven fabrics by finite element method

Cited by 27 publications

References 25 publications

Effective textile quality processing and an accurate inspection system using the advanced deep learning technique

Effective textile quality processing and an accurate inspection system using the advanced deep learning technique

Development of Experimental Setup for Measuring the Thermal Conductivity of Textiles

Three-dimensional analysis model of electric heating fabrics considering the skin metabolism

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