Artificial intelligence in the colour and textile industry

Shamey, Renzo; Hussain, Tanveer

doi:10.1111/j.1478-4408.2003.tb00142.x

Cited by 21 publications

(13 citation statements)

References 65 publications

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“…Shifting away from conventional processes and products will require a number of developments including: education of Shamey and Hussain (2003), DyStar (2010) Real-time on-line monitoring systems…”

Section: Discussionmentioning

confidence: 99%

“…In dyeing and finishing, expert systems are used for bleaching cotton fabrics, determining dyeing recipes, optimizing dyeing processes, executing textile finishing recipes and monitoring performance, selecting fluorescent whiteners, matching color and optimizing lab-to-bulk reproducibility, and managing the dyeing control system (Shim, 2009;Shamey and Hussain, 2003). Fuzzy logic is based on self-learning software systems, which auto-enlarges their knowledge using algorithms (EIPPCB, 2003).…”

Section: Fuzzy Logic and Other Expert Systems In The Textile Industrymentioning

confidence: 99%

“…The main strength of fuzzy logic is that it can address uncertainty and imprecision in decision-making processes, for example cotton color classification. A fuzzy inference system that uses fuzzy logic to classify cotton colors has been developed; the preliminary results show it is effective in reducing machine-classer disagreements about color grading, and that it shows good consistency over multiple years of cotton color data (Shamey and Hussain, 2003). Fuzzy logic also can be used to control the sizing process and the condensation reaction among cross-linking agents (EIPPCB, 2003;Shim, 2009).…”

Section: Fuzzy Logic and Other Expert Systems In The Textile Industrymentioning

confidence: 99%

“…A computer program has been developed to simulate the speed control of weaving machines using fuzzy logic. The program is based on an assessment of the weaver's load, the running behavior of the weaving machine, the speed range, and the shutdown frequency (Shamey and Hussain, 2003).…”

Section: Fuzzy Logic and Other Expert Systems In The Textile Industrymentioning

confidence: 99%

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A technical review of emerging technologies for energy and water efficiency and pollution reduction in the textile industry

Hasanbeigi

Price

2015

Journal of Cleaner Production

276

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a b s t r a c tThe textile industry uses large amounts of electricity, fuel, and water, with corresponding greenhouse gas emissions (GHGs) and contaminated effluent. Emerging energy-efficiency, greenhouse gas (GHG), and pollution mitigation technologies will be crucial for the textile industry as it responds to population and economic growth that is expected to spur a rapid increase in textile consumption over the coming decades and a corresponding increase in the industry's absolute energy use and GHG and other pollutant emissions. This paper gives an overview of textile industry processes and compiles available information on the energy savings, environmental and other benefits, costs, commercialization status, and references for 18 emerging technologies to reduce the industry's energy use and environmental emissions. Although studies from around the world identify a variety of sector-specific and cross-cutting energyefficiency technologies that have already been commercialized for the textile industry, information is scarce and/or scattered regarding emerging or advanced energy-efficiency and low-carbon technologies that are not yet commercialized or at the very early stage of adoption. This paper is intended to be a resource on these emerging technologies for engineers, researchers, investors, textile manufacturers, policy makers, and other interested parties.Published by Elsevier Ltd.

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Section: Discussionmentioning

confidence: 99%

Section: Fuzzy Logic and Other Expert Systems In The Textile Industrymentioning

confidence: 99%

Section: Fuzzy Logic and Other Expert Systems In The Textile Industrymentioning

confidence: 99%

Section: Fuzzy Logic and Other Expert Systems In The Textile Industrymentioning

confidence: 99%

See 2 more Smart Citations

A technical review of emerging technologies for energy and water efficiency and pollution reduction in the textile industry

Hasanbeigi

Price

2015

Journal of Cleaner Production

276

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“…The neural network system can solve a problem "by experience and learning" the input-output patterns provided by the user. In the field of textiles, artificial neural networks (mostly using back-propagation) have been extensively studied during the last two decades [4][5][6]. In the field of spinning previous research has concentrated on predicting the yarn properties and the spinning process performance using the fiber properties or a combination of fiber properties and machine settings as the input of neural networks [7][8][9][10][11][12].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Use of Artificial Neural Networks for Determining the Leveling Action Point at the Auto-leveling Draw Frame

Farooq

Cherif

2008

Textile Research Journal

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The evenness of the yarn plays an increasingly significant role in the textile industry, while the sliver evenness is one of the critical factors when producing quality yarn. The sliver evenness is also the major criteria for the assessment of the operation of the draw frame. In principle, there are two approaches to reduce the sliver irregularities. One is to study the drafting mechanism and recognize the causes for irregularities, so that means may be found to reduce them. The other more valuable approach is to use auto-levelers [1], since in most cases the doubling is inadequate to correct the variations in sliver. The control of sliver irregularities can lower the dependence on card sliver uniformity, ambient conditions, and frame parameters.At the auto-leveler draw frame (RSB-D40) the thickness variations in the fed sliver are continually monitored by a mechanical device (a tongue-groove roll) and subsequently converted into electrical signals. The measured values are transmitted to an electronic memory with a variable, the time delayed response. The time delay allows the draft between the mid-roll and the delivery roll of the draw frame to adjust exactly at that moment when the defective sliver piece, which had been measured by a pair of scanning rollers, finds itself at a point of draft. At this point, a servo motor operates depending upon the amount of variation detected in the sliver piece. The distance that separates the scanning rollers pair and the point of draft is called the zero point of regulation or the leveling action point (LAP) as shown in Figure 1. This leads to the calculated correction on the corresponding defective material [2,3]. In auto-leveling draw frames, especially in the case of a change of fiber material, or batches the machine settings and process controlling parameters must be optimized. The LAP is the most important auto-leveling parameter which is influenced by various parameters such as feeding speed, material, break draft gauge, main draft gauge, feeding tension, break draft, and setting of the sliver guiding rollers etc. 1 Previously, the sliver samples had to be produced with different settings, taken to the laboratory, and examined on Abstract Artificial neural networks with their ability of learning from data have been successfully applied in the textile industry. The leveling action point is one of the important auto-leveling parameters of the drawing frame and strongly influences the quality of the manufactured yarn. This paper reports a method of predicting the leveling action point using artificial neural networks. Various leveling action point affecting variables were selected as inputs for training the artificial neural networks with the aim to optimize the auto-leveling by limiting the leveling action point search range. The Levenberg-Marquardt algorithm is incorporated into the back-propagation to accelerate the training and Bayesian regularization is applied to improve the generalization of the networks. The results obtained are quite promising.

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