Fault detection and quality assessment in textiles by means of neural nets

Quality assessment and fault detection are important topics in textile research. Human assessment in this field, however, is subjective and slow. Presents an automatic assessment using two fundamentally different kinds of neural networks: the Kohonen Map (an unsupervised system) and the backpropagation network (supervised system). Evaluates two case studies using these techniques: assessment of carpet wear and the assessment of set marks. Both show good results when applied to the aforementioned problems. Makes a comparison between the two techniques and shows that the unsupervised system also gives an evaluation of the objectivity of the human experts.