Application of artificial neural network (ANN) for the prediction of thermal resistance of knitted fabrics at different moisture content

Kanat, Zehra; Özdіl, Nіlgün

doi:10.1080/00405000.2017.1423003

Cited by 39 publications

(34 citation statements)

References 36 publications

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“…Artificial neural networks have been the employed in the textile industry for more than two decades now. The neural networks have been the topic of various research studies in the textile industry like prediction of tensile properties of ternary blended open-end yarn [19], thermal resistance of knitted fabrics [20], segregation of cotton bales on its fibre attributes in yarn properties [21], classification of card-web defects [22], predicting the levelling action point at draw frame [23], control of sliver evenness [24] and predicting the spin ability of the yarn [25]. Similarly, artificial neural network can be used to model the spinning process by taking the machine settings and fibre quality parameters [26] and fibre to yarn predictions [27] as the input.…”

Section: Mh = 386 Mic 2 + 1816mic + 13 (4)mentioning

confidence: 99%

Predicting Cotton Fibre Maturity by Using Artificial Neural Network

Farooq

Sarwar

Ashraf

et al. 2018

Autex Research Journal

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Cotton fibre maturity is the measure of cotton’s secondary cell wall thickness. Both immature and over-mature fibres are undesirable in textile industry due to the various problems caused during different manufacturing processes. The determination of cotton fibre maturity is of vital importance and various methods and techniques have been devised to measure or calculate it. Artificial neural networks have the power to model the complex relationships between the input and output variables. Therefore, a model was developed for the prediction of cotton fibre maturity using the fibre characteristics. The results of predictive modelling showed that mean absolute error of 0.0491 was observed between the actual and predicted values, which show a high degree of accuracy for neural network modelling. Moreover, the importance of input variables was also defined.

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Section: Mh = 386 Mic 2 + 1816mic + 13 (4)mentioning

confidence: 99%

Predicting Cotton Fibre Maturity by Using Artificial Neural Network

Farooq

Sarwar

Ashraf

et al. 2018

Autex Research Journal

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“…There are many experimental and prediction models available to fulfill this need. Some researchers employed artificial neural networks (ANNs) models for thermal resistance predictions [15,16]. Hes and Loghin assumed thermal resistance of textile linked parallel to the thermal resistance of water in their suggested mathematical model [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Thermal Resistance Prediction of Different Composition Plain Socks in Wet State (Part 2)

Mansoor

Hes

Bajzík

2021

Autex Research Journal

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Socks’ comfort has vast implications in our everyday living. This importance increased when we have undergone an effort of low or high activity. It causes the perspiration of our bodies at different rates. In this study, plain socks with different fiber composition were wetted to a saturated level. Then after successive intervals of conditioning, these socks are characterized by thermal resistance in wet state at different moisture levels. Theoretical thermal resistance is predicted using combined filling coefficients and thermal conductivity of wet polymers instead of dry polymer (fiber) in different models. By this modification, these mathematical models can predict thermal resistance at different moisture levels. Furthermore, predicted thermal resistance has reason able correlation with experimental results in both dry (laboratory conditions moisture) and wet states.

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“…There must be a certain limit to the degree of heat and humidity of underwear, otherwise, there will be no comfort, but it will affect human health. However, at present, most researches on the influence of thermal and wet comfort of underwear on physiological activities only focus on the thermal and wet properties of fabrics [6][7][8][9][10][11][12], which does not involve the thermal and wet state of human body when wearing underwear. Even if some scholars have studied the thermal and wet comfort of underwear, they only use a kind of fabric or only study the static thermal and wet comfort.…”

Section: Introductionmentioning

confidence: 99%

Study on the influence of underwear on local thermal and moisture comfort of human body

2021

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In order to study the influence of underwear on human thermal and moisture comfort in different sports conditions, the objective and subjective evaluation of underwear (including undershirt and underpants) made of three kinds of fabrics were carried out, and the underwear comfort model was established by computational fluid dynamics, and the application prospect of computational fluid dynamics technology in the field of clothing comfort research was prospected. The results show: 1. the underwear combination of different fabrics has certain influence on the thermal and moisture comfort of human body, and the thermal and moisture of fabric of composite fiber is better than that of fabric of single fiber; 2. the temperature and humidity of chest, back, hip and thighs of the human body in different motion states change, and the temperature and humidity of the chest and back change greatly; 3. computational fluid dynamics can accurately predict human skin temperature.

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Application of artificial neural network (ANN) for the prediction of thermal resistance of knitted fabrics at different moisture content

Cited by 39 publications

References 36 publications

Predicting Cotton Fibre Maturity by Using Artificial Neural Network

Predicting Cotton Fibre Maturity by Using Artificial Neural Network

A New Approach for Thermal Resistance Prediction of Different Composition Plain Socks in Wet State (Part 2)

Study on the influence of underwear on local thermal and moisture comfort of human body

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