Comparison of high-volume instrument and advanced fiber information systems based on prediction performance of yarn properties using a radial basis function neural network

Turhan, Yıldıray; Toprakci, Ozan

doi:10.1177/0040517512445334

Cited by 13 publications

(7 citation statements)

References 39 publications

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“…In this regard, [28] the values of high-volume instrument (HVI) and advanced fiber information system (AFIS) as used as input parameters in the artificial neural network model. As cotton fibre maturity is the determining factor in fibre and textile processing as it has a direct relationship with fibre quality, it is measured by Advanced Fiber Information System (AFIS) [14].…”

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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“…A bundle sample from all commercial bales of cotton produced in the United States is examined with a high volume instrument (HVI) as part of a cotton classification system [1][2][3]. Classing of cotton determines marketable fiber properties, such as fiber strength, length, micronaire, trash content (grade), and uniformity [2,4,5]. In addition, the advanced fiber information system (AFIS) can provide information on fiber length and maturity [2,3].…”

Section: Introductionmentioning

confidence: 99%

Infrared Imaging of Cotton Fiber Bundles Using a Focal Plane Array Detector and a Single Reflectance Accessory

Cintrón

Montalvo

Hoven

et al. 2016

Fibers

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Infrared imaging is gaining attention as a technique used in the examination of cotton fibers. This type of imaging combines spectral analysis with spatial resolution to create visual images that examine sample composition and distribution. Herein, we report on the use of an infrared instrument equipped with a reflection accessory and an array detector system for the examination of cotton fiber bundles. Cotton vibrational spectra and chemical images were acquired by grouping pixels in the detector array. This technique reduced spectral noise and was employed to visualize cell wall development in cotton fibers bundles. Fourier transform infrared spectra reveal band changes in the CO bending region that matched previous studies. Imaging studies were quick, relied on small amounts of sample and provided a distribution of the cotton fiber cell wall composition. Thus, imaging of cotton bundles with an infrared detector array has potential for use in cotton fiber examinations.

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confidence: 99%

“…Turhan and Toprakci used ANN models to predict yarn characteristics. 5 They compared the prediction performances of high volume instruments (HVI) and advanced fiber information systems (AFIS) by radial functioned neural networks. They developed a neural network model that can predict the tension and hairiness of yarns and this model was fed with the parameter outputs of HVI and AFIS.…”

mentioning

confidence: 99%

“…Furthermore, by knowing the relationships between the performance and characteristics of yarns and fibers, the producer can make more accurate decisions on the fiber used in production for the quality of yarn required by the buyer. 5 Recent developments in the textile industry show that expert systems can help planners manage production lines. Artificial neural networks (ANN) have been found to be helpful in expert systems for resolving many problems in textiles, such as prediction of colorimetric values of fabrics, analysis of fabric defects, process optimization etc.…”

mentioning

confidence: 99%

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The prediction of quality characteristics of cotton/elastane core yarn using artificial neural networks and support vector machines

Doran

Şahin

2019

Textile Research Journal

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Core yarn is a type of yarn that has a filament fiber in the center with a different fiber wrapped around it. This type of yarn is of growing importance in the textile industry. It is important to predict the quality characteristics of a core yarn before production to prevent the faulty production of fabrics. Therefore, the development of predictive models is a necessity in the textile industry. In this study, artificial neural network (ANN) and support vector machine (SVM) models are proposed to predict the quality characteristics of cotton/elastane core yarn, using fiber quality and spinning parameters. Principal component analysis and analysis of variance techniques are also used to reduce input dimensions, since high dimensional data may reduce a model’s potential for success in prediction. The prediction models are trained and tested using the data obtained from a textile production plant. The results of all the models are compared with each other on test data. Mean absolute percentage error (MAPE), mean absolute error (MAE) and correlation coefficient (R) are used to assess the prediction power of the models. Although on most of the tests SVM models fared slightly better than ANN models, both models provide accurate predictions for most of the yarn quality characteristics. The results show that the best models have over 90% success rate in MAPE and R. In particular, the Coefficient of Variance of mass (CVm) along the yarn, hairiness and Reisskilometer quality characteristics of the cotton/elastane core yarn are predicted with 91%, 93% and 95% accuracy, respectively.

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Comparison of high-volume instrument and advanced fiber information systems based on prediction performance of yarn properties using a radial basis function neural network

Cited by 13 publications

References 39 publications

Predicting Cotton Fibre Maturity by Using Artificial Neural Network

Predicting Cotton Fibre Maturity by Using Artificial Neural Network

Infrared Imaging of Cotton Fiber Bundles Using a Focal Plane Array Detector and a Single Reflectance Accessory

The prediction of quality characteristics of cotton/elastane core yarn using artificial neural networks and support vector machines

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