Prediction of Cotton Yarn’s Characteristics by Image Processing and ANN

Abd-Elhamied, Manal R.; Hashima, Wael A.; ElKateb, Sherien; El-Hawary, I. A.; El-Geiheini, Adel

doi:10.1016/j.aej.2021.08.057

Cited by 9 publications

(7 citation statements)

References 12 publications

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“…Figure 8 shows the steps used to analyze the acquired images and to feed the acquired data into the neural network. At the level of artificial neural networks Manal R. Abd-Elhamied et al (2022) [12], from now on called System E, developed a project that allows a prediction of the characteristics of cotton thread by image processing and ANN (artificial neural network). It should be noted that the yarn characteristics obtained were taken with the USTER Tester 3, USTER Tester 5 and TENSORAPID4 machines.…”

Section: Use Of Artificial Intelligence Techniques In Yarn Analysismentioning

confidence: 99%

“…The ring yarn system had two hidden layers (20-8 neurons), and in addition, the compact system had only one layer with 12 neurons, as shown in Figure 9. [12]).…”

Section: Use Of Artificial Intelligence Techniques In Yarn Analysismentioning

confidence: 99%

“…Table 1 summarizes some of the characteristics of the previously proposed solutions compared to the case study proposed in the present research (Textile Yarn Prototype). [12]).…”

Section: Comparison With Similar Casesmentioning

confidence: 99%

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Intelligent Computer Vision System for Analysis and Characterization of Yarn Quality

et al. 2023

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The quality of yarn is essential in the control of the fabrics processes. There is some commercial equipment that measures the quality of yarn based on sensors, of different types, used for collecting data about some textile yarn characteristic parameters. The irregularity of the textile thread influences its physical properties/characteristics and there may be a possibility of a break in the textile thread during the fabric manufacturing process. This can contribute to the occurrence of unwanted patterns in fabrics that deteriorate their quality. The existing equipment, for the above-mentioned purpose, is characterized by its high size and cost, and for allowing the analysis of only few yarn quality parameters. The main findings/results of the study are the yarn analysis method as well as the developed algorithm, which allows the analysis of defects in a more precise way. Thus, this paper presents the development and results obtained with the design of a mechatronic prototype integrating a computer vision system that allows, among other parameters, the analysis and classification, in real time, of the hairs of the yarn using artificial intelligence techniques. The system also determines other characteristics inherent to the yarn quality analysis, such as: linear mass, diameter, volume, twist orientation, twist step, average mass deviation, coefficient of variation, hairiness coefficient, average hairiness deviation, and standard hairiness deviation, as well as performing spectral analysis. A comparison of the obtained results with the designed system and a commercial equipment was performed validating the undertaken methodology.

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Section: Use Of Artificial Intelligence Techniques In Yarn Analysismentioning

confidence: 99%

“…The ring yarn system had two hidden layers (20-8 neurons), and in addition, the compact system had only one layer with 12 neurons, as shown in Figure 9. [12]).…”

Section: Use Of Artificial Intelligence Techniques In Yarn Analysismentioning

confidence: 99%

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Intelligent Computer Vision System for Analysis and Characterization of Yarn Quality

et al. 2023

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“…In another different study, Elhamid et al predicted the properties of conventional and compact ring cotton yarns with ANN and image processing. In their study, in order to predict yarn properties, images were taken and image analysis was used, and then the feature vectors were defined as the input of ANN 29 .…”

Section: Introductionmentioning

confidence: 99%

Tuning Drafting Zone Parameters for Polyester Yarn within a Ring Spinning System: Modeling and Optimization

Savadroodbari,

Razbin,

Hasani

et al. 2023

Preprint

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Most textile products incorporate yarn as a fundamental element in the production process. Among various yarn manufacturing methods, the ring spinning system stands out as a crucially employed technology due to its advantages including yarn quality, evenness, low hairiness, and ease of handling. The parameters of drafting zone in this technology have a great impact on the quality of yarns. Typically, tuning this drafting zone parameters is time-consuming and costly through trial and error method. This study introduces an algorithmic procedure based on response surface methodology (RSM), experimental modeling, and multi-objective optimization to reduce unevenness percentage (U%) and imperfection index (IPI). Input parameters, including cots hardness of front and back top rollers, spacer size, and break draft, are optimized. Results indicate superior prediction performance of the artificial neural network (ANN) (average vaule of TGF = 1.9996) compared to RSM (average value of TGF = 1.8668). Consequently, ANN is selected for optimization. Furthermore, coupling the genetic algorithm with two ANN-based models reduced IPI from 39 to 33.67 and a reduction from 9.73–9.67% occurred in terms of U%. The final setting of Input parameters were cots hardness of front roller of 70 shore and cots hardness of back roller of 76 shore, spacer size 2.8 mm, and break draft of 1.26. This method efficiently optimizes the drafting zone parameter, enhancing yarn quality.

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“…The Wang's group proposed an image processing-based technique to evaluate the yarn evenness and hairiness [12]. Manal's group used the image processing and machine learning to determine the characteristics such as yarn tenacity, elongation%, and coefficient of mass variation% of the cotton's yarn [13]. From these examples and many more in the different research fields, image processing techniques possess considerable impact to achieve automatic and accurate analysis.…”

Section: Introductionmentioning

confidence: 99%

MATLAB Algorithms for Diameter Measurements of Textile Yarns and Fibers through Image Processing Techniques

Abdelkader

2022

Materials

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Textile yarns are the fundamental building blocks in the fabric industry. The measurement of the diameter of the yarn textile and fibers is crucial in textile engineering as the diameter size and distribution can affect the yarn’s properties, and image processing can provide automatic techniques for faster and more accurate determination of the diameters. In this paper, facile and new methods to measure the yarn’s diameter and its individual fibers diameter based on image processing algorithms that can be applied to microscopic digital images. Image preprocessing such as binarization and morphological operations on the yarn image were used to measure the diameter automatically and accurately compared to the manual measuring using ImageJ software. Additionally, the image preprocessing, the circular Hough transform was used to count the individual fibers in a yarn’s cross-section and count the number of fibers. The algorithms were built and deployed in a MATLAB (R2020b, The MathWorks, Inc., Natick, Massachusetts, United States) environment. The proposed methods showed a reliable, fast, and accurate measurement compared to other different image measuring softwares, such as ImageJ.

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Prediction of Cotton Yarn’s Characteristics by Image Processing and ANN

Cited by 9 publications

References 12 publications

Intelligent Computer Vision System for Analysis and Characterization of Yarn Quality

Intelligent Computer Vision System for Analysis and Characterization of Yarn Quality

Tuning Drafting Zone Parameters for Polyester Yarn within a Ring Spinning System: Modeling and Optimization

MATLAB Algorithms for Diameter Measurements of Textile Yarns and Fibers through Image Processing Techniques

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