Ali Shams Nateri, Elham Hasanlou and Abbas Hajipour
This paper aims to investigate using scanner-based adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANNs) and polynomial regression methods for…
Abstract
Purpose
This paper aims to investigate using scanner-based adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANNs) and polynomial regression methods for prediction of silver nanoparticles (AgNPs) and dye concentrations on AgNP-treated silk fabrics.
Design/methodology/approach
For estimation of the dye and AgNPs concentration using image processing, the silk fabrics were scanned under the condition of 200 pixels per inch. The red green blue (RGB) values of scanned images were obtained after applying the median filter. Then, the relationship between scanner RGB values and dye and AgNPs concentrations were obtained by using artificial intelligence methods such as ANFIS and ANNs.
Findings
The best result was achieved by the ANFIS system for calculation concentration of dye with 0.07% error and concentration of AgNPs with 0.008 (gr/l) error. The obtained results indicate that the performance of the ANFIS system method is better than the other methods.
Originality/value
Using a scanner-based artificial intelligence technique for prediction of nanosilver and dye content on silk fabric.
Details
Keywords
Ali Shams Nateri, Elham Hasanlou and Abbas Hajipour
Artificial intelligence (AI) methods, such as genetic algorithm (GA) and adaptive neuro-fuzzy inference system (ANFIS), are capable of providing superior solutions for the…
Abstract
Purpose
Artificial intelligence (AI) methods, such as genetic algorithm (GA) and adaptive neuro-fuzzy inference system (ANFIS), are capable of providing superior solutions for the simulation and the modeling of complex problems. The purpose of this study is to estimate the dye and the silver nanoparticle (AgNP) concentrations of silver nanoparticle-treated silk fabrics by the aforementioned methods.
Design/methodology/approach
In this study, the color and the antimicrobial properties of silver nanoparticle-treated silk fabrics were matched by using the GA technique based on spectrophotometric color matching. The ANFIS method was also used; this method is based on the grid partitioning algorithm across four different methods. The first and second methods are provided for dye concentration prediction, and the third and the fourth methods are given for AgNP concentration prediction.
Findings
The mean of absolute error and root mean square (RMS) of the best dye concentration prediction by the ANFIS method based on the second method are 0.087 and 0.103, respectively. In addition, the mean of the absolute error and the RMS of the best results for AgNP concentration prediction by the ANFIS method by using the third method is 0.002 and 0.003, respectively. The obtained results indicate that the performance of the ANFIS method is better than the GA method.
Originality value
The simultaneous prediction of the color and the antimicrobial properties of silver nanoparticle-treated silk fabrics was performed by using the GA and the ANFIS. The suggested method led to acceptable accuracy for color and antibacterial matching.
Details
Keywords
Ali Shams Nateri and Laleh Asadi
The purpose of this study is evaluate the optical properties of polyacrylonitrile (PAN) nanofibers containing fluorescent agents such as fluorescent dye and carbon quantum dots…
Abstract
Purpose
The purpose of this study is evaluate the optical properties of polyacrylonitrile (PAN) nanofibers containing fluorescent agents such as fluorescent dye and carbon quantum dots (CQDs) by using image-processing technique of Fluorescence microscope image.
Design/methodology/approach
The fluorescence microscope image of the pure PAN, PAN/CQDs and PAN/fluorescent dye nanofibers composite was analyzed using several image-processing techniques such as color histogram, lookup table (LUT), Fourier transform, RGB profile and surface plot analysis.
Findings
The fluorescence microscope image indicates that the fluorescence emission of nanocomposites depends on the type of fluorescent agent. The fluorescence intensity of nanofiber containing CQDs is more than nanofiber containing fluorescent dye. Various image-processing methods provide similar results for optical property of nanocomposites. Analyzing the LUT, the blue value of CQDs/PAN nanocomposite image was significantly higher than other nanocomposites. This was confirmed by other methods such as Fourier transform, color histogram and 3D topography of the electrospun nanofibers. According to analysis of colorimetric parameters, higher negative value of b* indicates bluer color for CQDs/PAN nanofibers than other nanocomposites. The obtained results indicate that the image-processing technique can be used to evaluate the optical property of fluorescent nanocomposite.
Originality/value
This study evaluates the optical properties of fluorescent nanocomposites by using image-processing techniques such as Fourier transform, color histogram, RGB profiles, LUT, surface plot and histogram analysis.
Details
Keywords
Aiqin Gao, Hongjuan Zhang and Kongliang Xie
– The purpose of this paper is to synthesise a tetrakisazo reactive dye and to characterise its dyeing property to meet the demand for better black reactive dyes.
Abstract
Purpose
The purpose of this paper is to synthesise a tetrakisazo reactive dye and to characterise its dyeing property to meet the demand for better black reactive dyes.
Design/methodology/approach
The novel tetrakisazo navy-blue reactive dye based on 4,4′-diaminostilbene-2,2′-disulphonic acid was designed and synthesized. The dyeing behaviour of it on cotton fabric was discussed. The synergistic blackening effect and absorbance spectra were investigated by absorbance and reflectance spectra, K/S and colorimetric data.
Findings
The exhaustion and fixation of the designed reactive dye were higher than 20 per cent than those of the commercial reactive dye, CI Reactive Black 5. The novel reactive dye has complementary with Reactive Red SPB and Reactive Yellow C-5R in absorbance spectra from 360 to 700 nm. Three reactive dyes had synergistic effect in colour deepening properties. The dyed cotton fabric possessed high K/S value and low reflectance in the whole visual spectrum range from 360 to 700 nm.
Practical implications
Comparison with the commercial Reactive Black DN-RN, the blackness of the dyed fabrics with the mixture dyes was greatly improved and the fastness properties on cotton fabrics were also good.
Originality/value
The paper is an original research work. Because the mixture dyes had better blackness and good fastness properties, it would have wide application in the dyeing of cotton fabric.
Details
Keywords
Monica Puri Sikka, Alok Sarkar and Samridhi Garg
With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been…
Abstract
Purpose
With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing.
Design/methodology/approach
The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration.
Findings
AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances.
Originality/value
This research conducts a thorough analysis of artificial neural network applications in the textile sector.
Details
Keywords
A.S. Nateri and E. Ekrami
Application of ratio spectra derivative spectrophotometery to quantitative analyses of bicomponent dye mixtures.
Abstract
Purpose
Application of ratio spectra derivative spectrophotometery to quantitative analyses of bicomponent dye mixtures.
Design/methodology/approach
The binary mixtures of five textile dyes including yellow, scarlet, red, blue, and navy blue colors were analysed by ratio spectra derivative spectrophotometry. The absorption spectra of the binary mixtures, prepared in different ratios, were recorded between 400 and 700 nm. The obtained spectra were divided by a standard spectrum of each component of the binary mixtures, and then the derivative spectra were calculated. The amounts of dyes were determined by the measurements in the appropriate wavelengths in the range of 400‐700 nm.
Findings
The analysis of obtained results via the proposed derivative and normal methods show higher accuracy of the developed method in determination of dye contents. The proposed derivative technique was found to be easily applicable for the quantitative analysis of dyes with both overlapping and non‐overlapping spectra in their binary mixtures.
Practical implications
The developed method can be a simple and practical solution to the quantitative analysis of bicomponent dye solutions with overlapping spectra.
Originality/value
Ratio spectra derivative spectrophotometery is introduced as a new approach for achieving the higher accuracy of determination of dye concentration in bicomponent dye solutions. The developed strategy could be applied in color industries where fine resolution of bicomponent dye mixtures is needed.
Details
Keywords
This chapter offers an overview of the applications of artificial intelligence (AI) in the textile industry and in particular, the textile colouration and finishing industry. The…
Abstract
This chapter offers an overview of the applications of artificial intelligence (AI) in the textile industry and in particular, the textile colouration and finishing industry. The advent of new technologies such as AI and the Internet of Things (IoT) has changed many businesses and one area AI is seeing growth in is the textile industry. It is estimated that the AI software market shall reach a new high of over US$60 billion by 2022, and the largest increase is projected to be in the area of machine learning (ML). This is the area of AI where machines process and analyse vast amount of data they collect to perform tasks and processes. In the textile manufacturing industry, AI is applied to various areas such as colour matching, colour recipe formulation, pattern recognition, garment manufacture, process optimisation, quality control and supply chain management for enhanced productivity, product quality and competitiveness, reduced environmental impact and overall improved customer experience. The importance and success of AI is set to grow as ML algorithms become more sophisticated and smarter, and computing power increases.
Details
Keywords
Leyla Yıldırım and Özlenen Erdem Ìşmal
The purpose of this paper is to show the dyeing effect of banana peel on polyamide fabric by using various mordants and to reveal alternatives to metallic mordants.
Abstract
Purpose
The purpose of this paper is to show the dyeing effect of banana peel on polyamide fabric by using various mordants and to reveal alternatives to metallic mordants.
Design/methodology/approach
The simultaneous mordanting method was used in the dyeing process.
Findings
From environmental and economical points of view, this paper studies the use of a natural waste product in textile dyeing. Assessment of domestic organic wastes will provide new possibilities for valorization of biomaterials. It is concluded that colorimetric data are affected by the amount of plant used in extraction, amount and type of mordant and pH of dye bath. Tin II chloride ensured the lightest color shades. No alternative mordants could be presented to iron II sulfate and tin II chloride, as they generated completely different color shades. Acids can be an alternative to 0.8 g/L alum mordant. Ammonium sulfate and sodium acetate also generated similar colors to alum. Ammonium sulfate can be substituted for citric acid and alum. Banana peel can be considered as a natural dye source for polyamide elastane blend fabric.
Originality/value
Banana peel can be suggested as a natural colorant with good wash fastness for dyeing of polyamide elastane blend fabric.
Details
Keywords
A. Gayathri, P. Varalakshmi and M. G. Sethuraman
This study aims to develop multifunctional, namely, superhydrophobic, flame-retardant and antibacterial, coatings over cotton fabric, using casein as green-based flame-retardant…
Abstract
Purpose
This study aims to develop multifunctional, namely, superhydrophobic, flame-retardant and antibacterial, coatings over cotton fabric, using casein as green-based flame-retardant and silver nanoparticles as antibacterial agent by solution immersion method.
Design/methodology/approach
The cotton fabric is first coated with casein to make it flame-retardant. AgNPs synthesized using Cinnamomum zeylanicum bark extract is coated over the casein layer. Finally, stearic acid is used to coat the cotton to make it superhydrophobic. X-ray diffraction, transmission electron microscopy analysis and ultraviolet-visible spectroscopy are used to investigate the produced AgNPs. The as-prepared multifunctional cotton is characterized by scanning electron microscopy, energy dispersive X-ray analysis and attenuated total reflection-infrared studies. Flame test, limiting oxygen index test and thermogravimetric analyzer studies have also been performed to study the flame-retardant ability and thermal stability of treated fabric, respectively. The antibacterial effect of the coatings is evaluated by disc-diffusion technique. Water contact angle is determined to confirm the superhydrophobic nature of cotton fabric.
Findings
The outcomes of this study showed that the prepared multifunctional cotton fabric had maximum contact angle of greater than 150° with good flame retardancy, high thermal stability, greater washing durability and high antibacterial activity against the growth of Pseudomonas aeruginosa and Acinetobacter indicus. Additionally, the as-prepared superhydrophobic cotton showed an excellent oil–water separation efficiency.
Research limitations/implications
The trilayered multifunctional cotton fabric has limiting washing durability up to 20 washing cycles. Treated functional fabric can be used as an antibacterial, therapeutic, water repellent and experimental protective clothing for medical, health care, home curtains and industrial and laboratory purposes.
Originality/value
The study brings out the robustness of this method in the development of multifunctional cotton fabrics.
Details
Keywords
Tintu Jose Manicketh and Mannancheril Sebastian Francis
The paper aims to investigate the feasibility of developing natural dyes from the barks of Araucaria columnaris and leaves of Macaranga peltata, Averrhoa bilimbi. The paper also…
Abstract
Purpose
The paper aims to investigate the feasibility of developing natural dyes from the barks of Araucaria columnaris and leaves of Macaranga peltata, Averrhoa bilimbi. The paper also deals with the application of natural dyes in textile coloration.
Design/methodology/approach
Dye extraction was carried out using the aqueous method. The dyeability of the aqueous extract was assessed on cotton, silk and polyester yarns using different mordants (alum, acetic acid, CuSO4, lemon juice) and without mordant. UV–Visible spectral analysis and pH of different natural dyes were determined. Percent absorption, K/S values, CIELab values and fastness properties of the selected dyed yarns were also assessed.
Findings
The percentage values for dye exhaustion differed with various mordants. The K/S values were found to be influenced by the addition of mordants. Different hues were obtained with the usage of different mordants. Fastness results exhibited good to very good grades.
Research limitations/implications
The effective application of aqueous method of dye extraction in the study avoids solvent toxicity. The current results proved that the dyeing could be achieved at room temperature for different yarns (cotton, silk, polyester). At present, no report exists in the literature of research work on the extraction of natural dyes from the leaves of M. peltata, A. bilimbi and their dyeing potential on cotton, silk and polyester.
Practical implications
The present work offers new environment-friendly dye as well as simple dyeing method. Barks and leaves are promising sources of dye. Enormous availability of barks and leaves avoids the exploitation of the plant parts for the extraction of natural dyes.
Originality/value
The important feature of this study was the effective dyeing of natural and synthetic fibers at room temperature. The novel sources of natural dyes would contribute significantly to the existing knowledge of dyeing, and the natural dyes reduce the environmental impact of synthetic dyes.