Abstract
In this research, a new method is developed for grading various types of yarn for appearance using image analysis and an artificial neural network. The images of standard yarn boards were analyzed by image analysis and four different faults factors were defined and measured for each series of yarn counts. For each series of yarn counts, a neural network with one layer was trained by measured fault factors of standard boards. The trained neural networks were used for grading various types of yarns. The yarns were also graded by the conventional standard method. The results of grading various types of yarns by image analysis and conventional standard method are compared. We found a strong correlation between the results of grading by two methods. Whereas, in the image analysis method, the grading procedure is not dependent on yarn structure and raw materials, we concluded that it is possible to use this method for grading of any types of yarns based on their apparent features.
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