The Evaluation of Uster Hairiness Results with an Image Analysis Approach

In this study, the images of the yarns were taken using a stereomicroscope. MATLAB software was used in image processing studies. The recommended image acquisition and processing steps in previous studies were followed, and the obtained results from textural parameters of images were compared with the results of Uster H and sh. The highest correlation in Uster H hairiness was obtained in the entropy textural parameter of the Sobel technique. The highest correlation in Uster sh hairiness was obtained in the mean of matrix elements (mean2) from the textural parameters in the Sobel technique. In general, higher correlation results were found in Uster sh than in Uster H. It has been observed that the Uster H results have deficiencies in determining the hairiness of dyed yarns. The different from the literature, this study presents that among the hairiness parameters, Uster sh shows the values closest to the real.

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Seçilmiş İpliklerde Zweigle İplik Tüylülüğü Sonuçlarına Farklı Test Hızlarının Etkisi

ÖZET: Bu çalışmada, Zweigle G657 cihazında iplik tüylülüğü sonuçlarına farklı test hızlarının etkisi araştırılmıştır. Ölçümler, cihazda seçenek olarak sunulan 25 m/dk, 50 m/dk, 100 m/dk ve 400 m/dk olmak üzere dört farklı test hızında gerçekleştirilmiştir. Test hızının etkisi SPSS programı kullanılarak istatistiki olarak incelenmiştir. İplik tüylülüğü sonuçları üzerinde, test hızındaki değişimin direkt olarak etkili olduğu görülmüştür. Tüylerin sınıflandırılmasında 10 mm’ye kadar, test hızları arasındaki farkın önemli seviyede olduğu görülmüştür. Aralarındaki bu farklılıklar 10 mm’den sonra önemsiz düzeye gelmiştir. Test hızı arttıkça ipliğin S3 tüylülük değerlerinde genel olarak bir artış eğilimi gözlenmiştir. Ancak bu çalışmada literatürden farklı olarak, çalışma için seçilen farklı yapıdaki ipliklerde farklı sonuçlarda elde edilmiştir. Farklı hızlarda elde edilen sonuçların karşılaştırma amaçlı kullanılmasının doğru olmayacağı net olarak görülmektedir. Yeni versiyon cihazlarda daha yüksek test hızlarına çıkma eğilimi bulunmaktadır. Bu nedenle, S3 tüylülüğü bilgisi verilirken, hangi cihazda ve hangi test hızında ölçüldüğü bilgisinin verilmesi gerektiği çıkarımı yapılmıştır.

The Effect of Different Test Speed on Zweigle Yarn Hairiness Results in Selected Yarns

ABSTRACT: In this study, the effect of different test speeds on yarn hairiness results in the Zweigle G657 device was investigated. Measurements were carried out at four different test speeds of 25 m/min, 50 m/min, 100 m/min and 400 m/min, which are available as options on the device. The effect of test speed was analyzed statistically using the SPSS program. It has been seen that the change in test speed has a direct effect on the yarn hairiness results. It has been observed that the difference between test speeds up to hairs in 10 mm in the classification of hair numbers is significant. These differences between them became insignificant after hairs in 10 mm. It can be generally said that an increasing trend was observed in the yarn S3 hairiness values as the test speed increased. However, different results were obtained in the yarns of different structures selected for the study different from the literature. It is clearly seen that it would not be appropriate to use the obtained results from different speeds for various comparisons. There is a tendency for higher test speeds on newer version devices. For this reason, it has been deduced that it is necessary to give the information on which device and at which test speed it was measured while giving the S3 hairiness information.

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The Comparison Of The Yarn Hairiness Test Devices Using The Hairiness Length Classification System

Yarn hairiness is an important element of total quality control. Zweigle system and its different versions are widely used commercially in the industry for the determination of yarn hairiness and these devices are only on hairiness. The measurement sensor on them classifies the hairs according to their length. In this research, Zweigle G567 and Uster Zweigle HL400 using the hairiness length classification system were compared. The most important difference between the two devices is that the recommended measuring speed for Uster Zweigle HL400 is eight times higher than Zweigle G567. In the study, thirteen yarns in different structures were used. The hairiness results in each mm were evaluated statistically in the SPSS program. It was observed that there were significant differences between the measurement results of two devices. In the literature, it is stated that there is an increase in the number of hairs with the increase in test speed of the Zweigle series hairiness devices. However, it was found in this study that there was a decrease in the number of hairs in most of the yarns measured in HL400 that use higher test speed. The more surprising result was that the strong correlation was determined between G567 and HL400 although the hair number obtained from devices show significant differences. This shows that the devices gave correlated results according to its operating principle, but the results of two devices operating at different speeds should not be compared with each other on the same test parameters.

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The Comparison of the Edge Detection Methods in the Determination of Yarn Hairiness through Image Processing

The resolution, quality and speed of the cameras have improved enormously in recent years. The combination of camera advancements and the software industry offers significant opportunities. 

In this study, an image processing approach for the determination of yarn hairiness was presented. Yarn images taken under a microscope were examined in MATLAB software. 

Seven different edge detection algorithms were used in order to separate the hairs from the yarn body. Seven different textural properties of obtained yarn images were compared with Zweigle hairiness test results. The findings have indicated that yarn hairiness can be clearly detected from microscope images with a six-step algorithm. 

The first four phases are grayscale, double format, 2D median filtering and histogram-fitting, respectively. The fifth stage is the edge detection algorithm and the sixth stage is the use of textural parameters. When compared with the Zweigle hairiness results, the most obvious finding to emerge from this study is that the best appropriate technique for edge detection was the Sobel method, and the textural parameter to be used in the evaluation was the standard deviation of matrix elements.

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