A Quality Assurance System Using Neural Networks

Edson Pacheco Paladini, Universidade Federal de Santa Catarina

This paper concentrates on the structure of a system to be used in the study of products that change some of their properties during the use they are submitted. The system has been applied to inspect products that have changed some of their main characteristics when they are been used in normal conditions. The color of textiles (clothes, for instance) in an example of this kind of situation. The approach is based on pattern recognition techniques and learning methods are used in this application. The system first analysis the original characteristics of the properties and then studies a set of images of the material during its use, after some days or months. A classification system is developed to define if the changes during this period are acceptable or not. The evaluation is made according to the classification that the system has learnt. A neural network is used in this system. Starting with the analysis made in the material in the moment it leaves the factory (patterns), we follow the pieces during the use, comparing them with the patterns. The neural network decides if the changes are acceptable or not.


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