Determination of Air Permeability Property of Air-Laid Nonwoven Fabrics Using Regression Analyses

Çelik, Halil İbrahim

doi:10.21533/pen.v5i2.125

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…As other fabric properties such as fabric count, weight, thermal resistance, and evaporative resistance are mutually dependent on thickness and/or air permeability, it has been hypothesized that the selected two fabric properties can properly represent the linear regression with performance. Different studies used linear regression analysis to find out the relation between fabric properties and performance [ 49 , 50 ]. For the statistical analysis, the mentioned fabric properties in Table 1 were considered as independent variables (input) and the amount of moisture and air gap size were the ordinal independent parameters.…”

Section: Methodsmentioning

confidence: 99%

Using Artificial Neural Network Modeling to Analyze the Thermal Protective and Thermo-Physiological Comfort Performance of Textile Fabrics Used in Oilfield Workers’ Clothing

Mandal

Mazumder

Agnew

et al. 2021

IJERPH

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Most of the fatalities and injuries of oilfield workers result from inadequate protection and comfort by their clothing under various work hazards and ambient environments. Both the thermal protective performance and thermo-physiological comfort performance of textile fabrics used in clothing significantly contribute to the mitigation of workers’ skin burns and heat-stress-related deaths. This study aimed to apply the ANN modeling approach to analyze clothing performance considering the wearers’ sweat moisture and the microclimate air gap that is generated in between their body and clothing. Firstly, thermal protective and thermo-physiological comfort performance of fire protective textiles used in oilfield workers’ clothing were characterized. Different fabric properties (e.g., thickness, weight, fabric count), thermal protective performance, and thermo-physiological comfort performance were measured. The key fabric property that affects thermal protective and thermo-physiological performance was identified as thickness by statistical analysis. The ANN modeling approach could be successfully implemented to analyze the performance of fabrics in order to predict the performance more conveniently based on the fabric properties. It is expected that the developed models could inform on-duty oilfield workers about protective and thermo-physiological comfort performance and provide them with occupational health and safety.

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Section: Methodsmentioning

confidence: 99%

Using Artificial Neural Network Modeling to Analyze the Thermal Protective and Thermo-Physiological Comfort Performance of Textile Fabrics Used in Oilfield Workers’ Clothing

Mandal

Mazumder

Agnew

et al. 2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…Table (8) shows that model ( 3) is an acceptable tool to predict fabric stiffness from proposed factors that affecting fabric pattern because all values of error are closed to (0) that indicate to a high performance of prediction model. Table (9) displays that model ( 3) is the best model of prediction performance by applying regression analysis to predict fabric stiffness. It gets high percentage of explanation between fabric factors and fabric stiffness that means higher values of R-Squared (0.939) and (MSE = 1.06E-05).…”

Section: Modelmentioning

confidence: 99%

“…In addition, Çelik, H. İ. [9] depended on image processing to get porosity ratios of air-laid nonwoven fabric samples. The research tried to obtain numerical value of porosity based on applying image processing.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Fabric Stiffness Based On Fabric Construction

Mustafa,

Malek,

El Gholmy

et al. 2023

Industrial Technology Journal

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This research presents a statistical tool to predict fabric stiffness based on fabric construction to avoid material waste, effort and time consuming in laboratories testing. Three different blended material of fabric were used that made from wool and polyester with different percentages of material yarns, counts and type (single/ply) of weft yarn, and fabric weave pattern (plain, twill and satin). Quantitative and qualitative variables were used in prediction by applying regression analysis and Artificial Neural Network (ANN) with a suitable coding system for different variables. Three independent models were derived to predict fabric stiffness by using regression analysis and compared to ANN model. Model (3) is the best prediction tool depending on regression analysis to predict fabric stiffness due to higher interpretation of the relation between variables used in prediction of fabric stiffness. Whereas, ANN achieved higher performance of validation (MSE = 7.41E-05) and total value of (R 2 = 0.967) when comparing to regression analysis model.

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“…It has been hypothesized that these fabric properties can represent linear regression with tensile strength. Different studies were found where linear regression analysis was used to model the relation between fabric properties and performance [43,44].…”

Section: Analyzing the Experimental Datamentioning

confidence: 99%

Characterizing the Tensile Strength of the Fabrics Used in Firefighters’ Bunker Gear under Radiant Heat Exposure

et al. 2022

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More than 60,000 firefighters’ injuries were reported by the National Fire Protection Association in the U.S. in 2019. Inadequate protection by bunker gear could be a reason for most of the injuries. Firefighters repeatedly encounter thermal hazards due to their job responsibilities. Degradation could occur on bunker gear fabric during thermal exposure. It has been found that the presence of moisture affects performance as well, which may come from wearers’ sweat. Proper evaluation of the tensile strength of the fabrics used in bunker gear could provide information essential for maintenance the overall integrity of the gear. An evaluation of the tensile strength of fabrics when exposed to 10, 15, and 20 kW/m2 radiant heat flux in the presence of moisture is reported. In each fabric system, a total of sixty-four different samples were prepared for four different types of fabric and four levels of moisture which were exposed to three different radiant heat flux for five minutes. Heat flux and moisture levels have significant impact on tensile strength. The effect of moisture on tensile strength in a three-layered fabric system is higher than that for a single layer fabric. An understanding of the impact of heat and moisture on fabric strength has been achieved.

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Determination of Air Permeability Property of Air-Laid Nonwoven Fabrics Using Regression Analyses

Cited by 6 publications

References 13 publications

Using Artificial Neural Network Modeling to Analyze the Thermal Protective and Thermo-Physiological Comfort Performance of Textile Fabrics Used in Oilfield Workers’ Clothing

Using Artificial Neural Network Modeling to Analyze the Thermal Protective and Thermo-Physiological Comfort Performance of Textile Fabrics Used in Oilfield Workers’ Clothing

Prediction of Fabric Stiffness Based On Fabric Construction

Characterizing the Tensile Strength of the Fabrics Used in Firefighters’ Bunker Gear under Radiant Heat Exposure

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