Part II. Predicting the Pilling Tendency of the Cotton Interlock Knitted Fabrics by Artificial Neural Network

Kayseri, Gonca Özçelik; Kirtay, Erhan

doi:10.1177/155892501501000417

Cited by 12 publications

(10 citation statements)

References 20 publications

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“…The obtained results revealed that DPCANNs had above average classification efficiency for pilling behaviour of knitted fabric. Another important work using ANN was performed by Kayseri et al where pilling tendency was predicted by selecting fabric cover factor as an input parameter [58]. They observed that by changing cover factor, fabric pilling was controlled to a greater extent.…”

Section: Fabric Type and Pilling Behaviourmentioning

confidence: 99%

Classification of Textile Polymer Composites: Recent Trends and Challenges

2021

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Polymer based textile composites have gained much attention in recent years and gradually transformed the growth of industries especially automobiles, construction, aerospace and composites. The inclusion of natural polymeric fibres as reinforcement in carbon fibre reinforced composites manufacturing delineates an economic way, enhances their surface, structural and mechanical properties by providing better bonding conditions. Almost all textile-based products are associated with quality, price and consumer’s satisfaction. Therefore, classification of textiles products and fibre reinforced polymer composites is a challenging task. This paper focuses on the classification of various problems in textile processes and fibre reinforced polymer composites by artificial neural networks, genetic algorithm and fuzzy logic. Moreover, their limitations associated with state-of-the-art processes and some relatively new and sequential classification methods are also proposed and discussed in detail in this paper.

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Section: Fabric Type and Pilling Behaviourmentioning

confidence: 99%

Classification of Textile Polymer Composites: Recent Trends and Challenges

2021

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“…Pilling grades of samples are evaluated by the eye. Furthermore, this evaluation should be supported by standard photos [1][2][3][4][5]. Most of the time in decision making, it is difficult to obtain same results because of differences in visual perception between experts.…”

Section: Introductionmentioning

confidence: 99%

An Image Processing Research Consistent With Standard Photographs to Determine Pilling Grade of Woven Fabrics

Telli

2019

Tekstil Ve Konfeksiyon

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In this study, MATLAB 2018a software was used to evaluate pilling grade of woven fabrics objectively. Experimental works were carried out on the EMPA W3 standard photographs and accordingly two woven fabrics. Equations were built based on the measurements of pill characteristics and textural parameters of these photographs with the help of curve fitting method after image processing steps. Intervals were generated for each fabric by using slope of these equations and quantitative parameters obtained from the original fabric. Furthermore, fabrics that were provided pilling formation at different test turns were evaluated subjectively by expert operators.Objective results corresponding to each parameter were analyzed comparatively with these subjective results. The developed method was successful by using mean of matrix elements from textural parameters and total area from pill characteristics. ARTICLE HISTORY

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“…Gurkan Unal used artificial neural network and response surface methods for evaluating the effects fiber properties on spliced yarn characteristics (21,22). For predicting the pilling tendencies of cotton interlock knitted fabrics, Kayseri used both regression analysis and artificial neural network methods in their studies (23,24). Sari and Oglakcioglu also used regression analysis in their study about pressure characteristics of medical stockings (25).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Tensile Strength and Unevenness of Compact-Spun Yarns by Using Hvi Fiber Properties

Üzümcü¹,

Kadoğlu²

2018

Tekstil Ve Konfeksiyon

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Compact spinning system, which has begun to gain an important place in textile industry, thanks to its desired yarn characteristics, was developed in the early 2000s. Less hairy yarns can especially be produced with this system, as stated in the literature. Moreover, the system has significant advantages in terms of tensile strength. In this study, yarns were produced with different yarn counts (Ne 40 -Ne 90) and different twist coefficients (αe 3.8, αe 4.2 and αe 4.6) using the compact spinning system. Regression analysis was carried out using the test results of the yarns produced and HVI fiber properties which were measured before the production. It was investigated whether the properties of the yarns can be estimated before production by using specified fiber properties. As a result of the analysis, the derived equations were used to estimate tensile strength and unevenness values of the yarns.

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Part II. Predicting the Pilling Tendency of the Cotton Interlock Knitted Fabrics by Artificial Neural Network

Cited by 12 publications

References 20 publications

Classification of Textile Polymer Composites: Recent Trends and Challenges

Classification of Textile Polymer Composites: Recent Trends and Challenges

An Image Processing Research Consistent With Standard Photographs to Determine Pilling Grade of Woven Fabrics

Estimation of Tensile Strength and Unevenness of Compact-Spun Yarns by Using Hvi Fiber Properties

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