Application of Neural Networks (NNs) for Fabric Defect Classification

The defect classification is as important as the defect detection in fabric inspection process. The detected defects are classified according to their types and recorded with their names during manual fabric inspection process. The material is selected as “undyed raw denim” fabric in this study. Four commonly occurring defect types, hole, warp lacking, weft lacking and soiled yarn, were classified by using artificial neural network (ANN) method. The defects were automatically classified according to their texture features. Texture feature extraction algorithm was developed to acquire the required values from the defective fabric samples. The texture features were assessed as the network input values and the defect classification is obtained as the output. The defective images were classified with an average accuracy rate of 96.3%. As the hole defect was recognized with 100% accuracy rate, the others were recognized with a rate of 95%

Fabric defect detection using linear filtering and morphological operations

An algorithm with linear filters and morphological operations has been proposed for automatic fabric defect detection. The algorithm is applied off-line and real-time to denim fabric samples for five types of defects. All defect types have been detected successfully and the defective regions are labeled. The defective fabric samples are then classified by using feed forward neural network method. Both defect detection and classification application performances are evaluated statistically. Defect detection performance of real time and off-line applications are obtained as 88% and 83% respectively. The defective images are classified with an average accuracy rate of 96.3%

Kinematic analysis of beat-up mechanism used for handmade carpet looms

129-136Kinematic analysis and dimensional synthesis of a beat-up mechanism used for handmade carpet looms have been studied. The design criteria of the beat-up mechanism has been established according to the problem statement, followed by the selection of a crank-rocker type four-link mechanism for the beat-up mechanism to obtain many crank-rocker type mechanisms using dimensional synthesis method. On the basis of the design criteria, the most suitable beat-up mechanism is chosen and the dynamic analysis of the selected mechanism is performed. In the dimensional synthesis, the case studies have been done for four different crank rotation angles and the most proper dimensions according to design criteria are obtained at = 180°. In the dynamic analysis of the mechanism, it is determined that the beat-up force of the mechanism is over 60 N. By designing such a suitable beat-up mechanism for handmade carpet looms, the weaver gets less tired, the handmade carpet production is increased and the faults caused by this process are decreased

Dokuma kumaşlarda doku tipinin aşınma ve boncuklanma dayanımı üzerine etkilerinin araştırılması

In this experimental study, abrasion resistance properties of woven fabrics are investigated as a function of weave type. Seven woven fabrics with different weave derivatives are woven with 100% cotton and Ne 30/1 combed ring spun yarn for this investigation. These fabrics are tested with Martindale Abrasion Tester to determine the abrasion and pilling resistance properties. the pilling resistance of fabrics was evaluated by numbering the pills in 1cm2 area of the tested fabric and abrasion resistance of the fabrics was evaluated according to their weight loss ratio after 15000 cycle of Martindale Abrasion testing device. According to data obtained from test results of sample weave patterns we observed that weave pattern has an important effect on the abrasion and pilling resistance property of woven fabrics. the weave types that have low number of floats and high number of interfacings exhibited high abrasion and pilling resistance. ANOVA results also showed that weave pattern has a significant effect on these properties. By Tukey test, weave types are grouped according to their effect on abrasion resistance.Bu deneysel çalışmada, dokuma kumaşların aşınma ve boncuklanma dayanımları kumaş doku tipinin bir fonksiyonu olarak incelenmiştir. Araştırma için % 100 pamuk, Ne 30/1 penye ring ipliğinden 7 farklı doku tipine sahip kumaş dokunmuştur. Kumaşların aşınma ve boncuklanma dayanımlarının tespit edilmesi amacıyla Martindale Aşınma ve Boncuklanma deney cihazı kullanılmıştır. Boncuklanma dayanımı cihazın 2000 devri sonucunda kumaşın 1 cm2'sinde gözlemlenen ortalama boncuk sayısı ile tespit edilmiştir. Aşınma dayanımı ise cihazın 15000 devri sonucunda numunelerin ortalama ağırlık kaybının ilk ağırlıklanna oranının yüzdesi olarak ifade edilmiştir. Testler sonucunda dokuma kumaşlarda doku tipinin aşınma ve boncuklanma dayanımı üzerinde önemli bir etkisi olduğu görülmektedir. Atlama sayısının az ve bağlantı sayısının fazla olduğu kumaşlarda aşınma ve boncuklanma dayanımları daha yüksektir. Deney sonuçlarını istatistiksel olarak incelemek üzere yapılan ANOVA analizi de dokuma kumaş doku tipinin aşınma ve boncuklanma dayanımları üzerine anlamlı bir etki yaptığını göstermektedir, Tukey testi yardımıyla, doku tipleri aşınma dayanımı üzerine yaptıkları etkiye göre gruplara ayrılmıştır

Fabric defect detection using linear filtering and morphological operations

254-259An algorithm with linear filters and morphological operations has been proposed for automatic fabric defect detection. The algorithm is applied off-line and real-time to denim fabric samples for five types of defects. All defect types have been detected successfully and the defective regions are labeled. The defective fabric samples are then classified by using feed forward neural network method. Both defect detection and classification application performances are evaluated statistically. Defect detection performance of real time and off-line applications are obtained as 88% and 83% respectively. The defective images are classified with an average accuracy rate of 96.3%