In this study, a machine vision system is developed to achieve fabric inspection and defect classification processes automatically. The system consists of an image acquisition hardware and an image processing software. A simple and portable system was designed so that it can be adapted easily to all types of the fabric inspection machines. The software of the system consists of defect detection and classification algorithms. The defect detection algorithm is based on wavelet transform, double thresholding binarization, and morphological operations. It was applied real time via a user interface prepared by using MATLAB ® program. The defect classification approach is based on gray level co-occurrence matrix and feed forward neural network. Five commonly occurring defect types, warp lacking, weft lacking, soiled yarn hole, and yarn flow, were detected and classified. The defective and defect-free regions of the fabric were detected with an accuracy of 93.4% and the defects are classified with 96.3% accuracy rate.

The appearance of cut-pile carpets deteriorates due to foot traffic and long term heavy loadings. This deterioration is generally seen as a fuzzy appearance due to loose fibers and fuzz on the carpet surface or as thickness loss. The fiber fineness has important effects on a number of yarn properties including cohesion, evenness, strength, stiffness and luster. In this study, three acrylic yarns with different fiber finenesses at the same yarn linear density were used to produce cut-pile carpet samples. The effects of fiber linear density on the amount of loose fibers and fuzz on the carpet surface, thickness loss under prolonged heavy static and dynamic loadings and compression recovery after loading-unloading were investigated. The carpet produced with finest fibers yarns had the lowest amount of loose fibers due to higher cohesion. The carpet samples with the finest fibers exhibited the highest resiliency and lowest thickness loss under static loading. However, the carpet samples with the coarsest fibers showed the least thickness loss under dynamic loading and higher compression recovery after loading-unloading.

Elastic core-spun yarns which is used as weft yarn for textile fabrics gained great importance in the last decade its due to the fact that stretch and recovery, comfort fits and flexibility properties. The technological progress made the dual core-spun yarn production possible. The dual core-spun yarns are composed of filament that contributes durability and polyurethane based elastane that provides stretchability to the fabrics. Hereby, both filament and elastane characteristics have great influence on denim performance at the same time. The main purpose of this study is to achieve the effect of filament fineness and elastane draft on denim fabric performance such as breaking force, breaking elongation, tear force, vertical elastic recovery, moisture management that is wicking rate and water absorption properties. Meanwhile, filament core-spun yarns with different filament fineness and 100% cotton yarn were also used as weft of the denims in order to investigate the differences statistically. It was found that that filament fineness and elastane draft had statistically significant effect on all inspected performances of denim fabrics except water absorption.

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