Modeling Ultraviolet Protection Factor of Polyester-Cotton Blended Woven Fabrics Using Soft Computing Approaches

2014 ◽  
Vol 9 (3) ◽  
pp. 155892501400900 ◽  
Author(s):  
Piyali Hatua ◽  
Abhijit Majumdar ◽  
Apurba Das
Keyword(s):  
Soft Computing ◽  
Fuzzy Inference ◽  
Woven Fabrics ◽  
Good Prediction ◽  
Protection Factor ◽  
Inference System ◽  
Ultraviolet Protection Factor ◽  
Good Prediction Accuracy ◽  
Input Parameters ◽  
Ultraviolet Protection

Ultraviolet protection factor (UPF) of woven fabrics is modeled by using two soft computing approaches, namely adaptive network based fuzzy inference system (ANFIS) and artificial neural network (ANN). Three fabric parameters: proportion of polyester in weft yarns, weft count, and pick density are used as input parameters for predicting fabric UPF. Two levels (low and high) of membership function for each of the input parameters are used to reduce the complexity of ANFIS. The eight linguistic fuzzy rules trained by ANFIS are able to explain the relationship between fabric parameters and UPF. A comparison between ANFIS and ANN models is also presented. Both the models predict the UPF of fabrics with very good prediction accuracy in the testing data sets.

scholarly journals Ultraviolet Protection by Fabric Engineering

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Mukesh Kumar Singh ◽  
Annika Singh
Keyword(s):  
Areal Density ◽  
Woven Fabrics ◽  
Uv Protection ◽  
Comfort Level ◽  
Protection Factor ◽  
Ultraviolet Protection Factor ◽  
Wear Comfort ◽  
Ultraviolet Protection ◽  
Multifilament Yarns ◽  
Fabric Structures

Background. The increasing emission of greenhouse gases has evoked the human being to save the ozone layer and minimize the risk of ultraviolet radiation (UVR). Various fabric structures have been explored to achieve desired ultraviolet protection factor (UPF) in various situations. Objective. In this study, the effect of various filament configurations like twisted, flat, intermingled, and textured in multifilament yarns on fabric in different combinations is assessed in order to engineer a fabric of better ultraviolet protection factor (UPF). Methods. In order to engineer a fabric having optimum UV protection with sufficient comfort level in multifilament woven fabrics, four different yarn configurations, intermingled, textured, twisted, and flat, were used to develop twelve different fabric samples. The most UV absorbing and most demanding fibre polyethylene terephthalate (PET) was considered in different filament configuration. Results. The combinations of intermingled warp with flat, intermingled, and textured weft provided excellent UVR protection comparatively at about 22.5 mg/cm2 fabric areal density. The presence of twisted yarn reduced the UV protection due to enhanced openness in fabric structure. Conclusion. The appropriate combination of warp and weft threads of different configuration should be selected judiciously in order to extract maximum UV protection and wear comfort attributes in multifilament woven PET fabrics.

Development and characterization of three-dimensional woven fabric for ultra violet protection

2018 ◽  
Vol 30 (4) ◽  
pp. 536-547
Author(s):  
Adeela Nasreen ◽  
Muhammad Umair ◽  
Khubab Shaker ◽  
Syed Talha Ali Hamdani ◽  
Yasir Nawab
Keyword(s):  
3D Structure ◽  
Three Dimensional ◽  
Air Permeability ◽  
Woven Fabrics ◽  
Woven Fabric ◽  
Protection Factor ◽  
Content Type ◽  
Ultraviolet Protection Factor ◽  
Ultraviolet Protection

Purpose The purpose of this paper is to investigate the effect of materials, three dimensional (3D) structure and number of fabric layers on ultraviolet protection factor (UPF), air permeability and thickness of fabrics. Design/methodology/approach Total 24 fabrics samples were developed using two 3D structures and two weft materials. In warp direction cotton (CT) yarn and in weft direction polypropylene (PP) and polyester (PET) were used. Air permeability, thickness and UPF testings were performed and relationship among fabric layers, air permeability, thickness and UPF was developed. Findings UPF and thickness of fabrics increases with number of fabric layers, whereas air permeability decreases with the increase in number of fabric layers. Furthermore, change of multilayer structure from angle interlock to orthogonal interlock having same base weave does not give significant effect on UPF. However, change of material from polyester (PET) to polypropylene (PP) has a dominant effect on UPF. Minimum of three layers of cotton/polyester fabric, without any aid of ultraviolet radiation (UV) resistant coating, are required to achieve good. Cotton/polyester fabrics are more appropriate for outdoor application due to their long-term resistance with sunlight exposure. Originality/value Long-term exposure to UV is detrimental. So, there is need of proper selection of material and fabric to achieve ultraviolet protection. 3D fabrics have yarns in X, Y as well as in Z directions which provide better ultraviolet protection as compared to two dimensional (2D) fabrics. In literature, mostly work was done on ultraviolet protection of 2D fabrics and surface coating of fabrics. There is limited work found on UPF of 3D woven fabrics.

scholarly journals A critical review of theoretical models for estimating global so-lar radiation between 2012-2016 in Nigeria

2017 ◽  
Vol 5 (2) ◽  
pp. 60 ◽  
Author(s):  
Samuel Nwokolo ◽  
Julie Ogbulezie
Keyword(s):  
Solar Radiation ◽  
Soft Computing ◽  
Fuzzy Inference ◽  
Theoretical Models ◽  
Global Solar Radiation ◽  
Empirical Models ◽  
Neural Network Models ◽  
Inference System ◽  
Input Parameters ◽  
Functional Forms

A routinely research of solar radiation is of vital requirement for surveys in agronomy, hydrology, ecology and sizing of the photovoltaic or thermal solar systems, solar architecture, molten salt power plant and supplying energy to natural processes like photosynthesis and estimates of their performances. However, measurement of global solar radiation is not available in most locations across in Nigeria. During the past 5 years in order to estimate global solar radiation on the horizontal surface on both daily and monthly mean daily basis, numerous empirical models have been developed for several locations in Nigeria. As a result, various input parameters have been utilized and different functional forms used. In this study aims at comparing, classifying and reviewing the empirical and soft computing models applied for estimating global solar radiation. The empirical models so far utilized were classified into eight main categories and presented based on the input parameters employed. The models were further reclassified into several main sub-classes and finally represented according to their developing year. On the whole, 145 empirical models and 42 functional forms, 8 artificial neural network models, 1 adaptive neural fuzzy inference system approach, and 1 Autoregressive Moving Average methods were recorded in literature for estimating global solar radiation in Nigeria. This review would provide solar-energy researchers in terms of identifying the input parameters and functional forms widely employed up until now as well as recognizing their importance for estimating global solar radiation using soft computing empirical models in several locations in Nigeria.

Modeling of cutting parameters in turning of PEEK composite using artificial neural networks and adaptive-neural fuzzy inference systems

2021 ◽  
pp. 089270572110130
Author(s):  
Gökçe Özden ◽  
Mustafa Özgür Öteyaka ◽  
Francisco Mata Cabrera
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cutting Forces ◽  
Fuzzy Inference ◽  
Test Results ◽  
Inference System ◽  
Good Prediction Accuracy ◽  
Peek Composite ◽  
Artificial Neural ◽  
Neural Fuzzy

Polyetheretherketone (PEEK) and its composites are commonly used in the industry. Materials with PEEK are widely used in aeronautical, automotive, mechanical, medical, robotic and biomechanical applications due to superior properties, such as high-temperature work, better chemical resistance, lightweight, good absorbance of energy and high strength. To enhance the tribological and mechanical properties of unreinforced PEEK, short fibers are added to the matrix. In this study, Artificial Neural Networks (ANNs) and the Adaptive-Neural Fuzzy Inference System (ANFIS) are employed to predict the cutting forces during the machining operation of unreinforced and reinforced PEEK with30 v/v% carbon fiber and 30 v/v% glass fiber machining. The cutting speed, feed rate, material type, and cutting tools are defined as input parameters, and the cutting force is defined as the system output. The experimental results and test results that are predicted using the ANN and ANFIS models are compared in terms of the coefficient of determination ( R2) and mean absolute percentage error. The test results reveal that the ANFIS and ANN models provide good prediction accuracy and are convenient for predicting the cutting forces in the turning operation of PEEK.

scholarly journals A Soft Computing Approach to Crack Detection and Impact Source Identification with Field-Programmable Gate Array Implementation

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Arati M. Dixit ◽  
Harpreet Singh
Keyword(s):  
Fuzzy Logic ◽  
Soft Computing ◽  
Fuzzy Inference System ◽  
Source Identification ◽  
Field Programmable Gate Array ◽  
Crack Detection ◽  
Fuzzy Inference ◽  
Inference System ◽  
Field Programmable ◽  
Computing Approach

The real-time nondestructive testing (NDT) for crack detection and impact source identification (CDISI) has attracted the researchers from diverse areas. This is apparent from the current work in the literature. CDISI has usually been performed by visual assessment of waveforms generated by a standard data acquisition system. In this paper we suggest an automation of CDISI for metal armor plates using a soft computing approach by developing a fuzzy inference system to effectively deal with this problem. It is also advantageous to develop a chip that can contribute towards real time CDISI. The objective of this paper is to report on efforts to develop an automated CDISI procedure and to formulate a technique such that the proposed method can be easily implemented on a chip. The CDISI fuzzy inference system is developed using MATLAB’s fuzzy logic toolbox. A VLSI circuit for CDISI is developed on basis of fuzzy logic model using Verilog, a hardware description language (HDL). The Xilinx ISE WebPACK9.1i is used for design, synthesis, implementation, and verification. The CDISI field-programmable gate array (FPGA) implementation is done using Xilinx’s Spartan 3 FPGA. SynaptiCAD’s Verilog Simulators—VeriLogger PRO and ModelSim—are used as the software simulation and debug environment.

Soft-Computing Control of Ball and Beam System

2018 ◽  
Vol 9 (4) ◽  
pp. 1-21 ◽  
Author(s):  
Ashwani Kharola ◽  
Pravin P. Patil
Keyword(s):  
Soft Computing ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Learning Ability ◽  
Gaussian Shape ◽  
Inference System ◽  
Neural Controllers ◽  
Beam System ◽  
Soft Computing Techniques ◽  
Ball And Beam System

This article derives a mathematical model and compares different soft-computing techniques for control of a highly dynamic ball and beam system. The techniques which were incorporated for control of proposed system were fuzzy logic, proportional-integral-derivative (PID), adaptive neuro fuzzy inference system (ANFIS) and neural networks. Initially, a fuzzy controller has been developed using seven gaussian shape membership functions. The article illustrates briefly both learning ability and parameter estimation properties of ANFIS and neural controllers. The results of PID controller were collected and used for training of ANFIS and Neural controllers. A Matlab simulink model of a ball and beam system has been derived for simulating and comparing different controllers. The performances of controllers were measured and compared in terms of settling time and steady state error. Simulation results proved the superiority of ANFIS over other control techniques.

scholarly journals Antimicrobial Activity and Barrier Properties against UV Radiation of Alkaline and Enzymatically Treated Linen Woven Fabrics Coated with Inorganic Hybrid Material

Molecules ◽  
2020 ◽  
Vol 25 (23) ◽  
pp. 5701
Author(s):  
Joanna Olczyk ◽  
Jadwiga Sójka-Ledakowicz ◽  
Anetta Walawska ◽  
Anna Antecka ◽  
Katarzyna Siwińska-Ciesielczyk ◽  
...  
Keyword(s):  
Antimicrobial Activity ◽  
Uv Radiation ◽  
New Technologies ◽  
Barrier Properties ◽  
Dip Coating ◽  
Sem Analysis ◽  
Woven Fabrics ◽  
Protection Factor ◽  
Ultraviolet Protection Factor ◽  
Harmful Effects

One of the directions of development in the textiles industry is the search for new technologies for producing modern multifunctional products. New solutions are sought to obtain materials that will protect humans against the harmful effects of the environment, including such factors as the activity of microorganisms and UV radiation. Products made of natural cellulose fibers are often used. In the case of this type of material, it is very important to perform appropriate pretreatment before subsequent technological processes. This treatment has the aim of removing impurities from the surface of the fibers, which results in the improvement of sorption properties and adhesion, leading directly to the better penetration of dyes and chemical modifiers into the structure of the materials. In this work, linen fabrics were subjected to a new, innovative treatment being a combination of bio-pretreatment using laccase from Cerrena unicolor and modification with CuO-SiO2 hybrid oxide microparticles by a dip-coating method. To compare the effect of alkaline or enzymatic pretreatment on the microstructure of the linen woven fabrics, SEM analysis was performed. The new textile products obtained after this combined process exhibit very good antimicrobial activity against Candida albicans, significant antibacterial activity against the Gram-negative Escherichia coli and the Gram-positive Staphylococcus aureus, as well as very good UV protection properties (ultraviolet protection factor (UPF) > 40). These innovative materials can be used especially for clothing or outdoor textiles for which resistance to microorganisms is required, as well as to protect people who are exposed to long-term, harmful effects of UV radiation.

scholarly journals A Comparative Study between Regression and Neural Networks for Modeling Al6082-T6 Alloy Drilling

Machines ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 13 ◽  
Author(s):  
Nikolaos Karkalos ◽  
Nikolaos Efkolidis ◽  
Panagiotis Kyratsis ◽  
Angelos Markopoulos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Model ◽  
Soft Computing ◽  
Regression Models ◽  
Fuzzy Inference ◽  
Process Conditions ◽  
Neural Network Models ◽  
Inference System ◽  
Computing Models

Apart from experimental research, the development of accurate and efficient models is considerably important in the field of manufacturing processes. Initially, regression models were significantly popular for this purpose, but later, the soft computing models were proven as a viable alternative to the established models. However, the effectiveness of soft computing models can be often dependent on the size of the experimental dataset, and it can be lower compared to that of the regression models for a small-sized dataset. In the present study, it is intended to conduct a comparison of the performance of various neural network models, such as the Multi-layer Perceptron (MLP), the Radial Basis Function Neural Network (RBF-NN), and the Adaptive Neuro-Fuzzy Inference System (ANFIS) models with the performance of a multiple regression model. For the development of the models, data from drilling experiments on an Al6082-T6 workpiece for various process conditions are employed, and the performance of models related to thrust force (Fz) and cutting torque (Mz) is assessed based on several criteria. From the analysis, it was found that the MLP models were superior to the other neural networks model and the regression model, as they were able to achieve a relatively lower prediction error for both models of Fz and Mz.

Anti-ultraviolet properties of β-cyclodextrin-grafted cotton fabrics dyed by broadleaf holly leaf extract

2020 ◽  
Vol 90 (21-22) ◽  
pp. 2441-2453 ◽  
Author(s):  
Jinshu Liu ◽  
Xiaoyan Ma ◽  
Wenzhao Shi ◽  
Jianwei Xing ◽  
Chaoqun Ma ◽  
...  
Keyword(s):  
Tensile Strength ◽  
Leaf Extract ◽  
Cotton Fabrics ◽  
Sodium Hypophosphite ◽  
Crosslinking Reaction ◽  
Ultraviolet Spectrophotometry ◽  
Protection Factor ◽  
Ultraviolet Protection Factor ◽  
Ultraviolet Protection ◽  
Macerated Extract

The aim of the study was to investigate the anti-ultraviolet properties of β-cyclodextrin-grafted cotton fabrics dyed with broadleaf holly leaf extract. Flavonoids were extracted from broadleaf holly leaf by maceration and a stoichiometry of 1:1 inclusion complex with β-cyclodextrin was formed. Characterized by the fluorescence spectrum and ultraviolet spectrophotometry, the fluorescence intensities and ultraviolet absorption of the macerated extract were enhanced by increasing the amount of cyclodextrin. Cotton fabrics were grafted with β-cyclodextrin through a crosslinking reaction based on citric acid in the presence of sodium hypophosphite then dyed with the macerated extract of broadleaf holly leaf used as a natural ultraviolet absorbent. The anti-ultraviolet property of fabrics dyed by a macerated extract was characterized in terms of the ultraviolet protection factor. It was noted that the cotton fabrics grafted with β-cyclodextrin exhibit enhanced anti-ultraviolet and wrinkle recovery properties compared to the unmodified samples and show an excellent durability against 30 washing cycles, accompanied by a loss of tensile strength.

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