A non-conventional quality control system to detect surface faults in mechanical front seals
The Just in Time and the Total Quality policies have remarkably touched every field of modern industrial production. This context prompts companies to dedicate most of their efforts on researching and developing automatic systems of quality control to obtain the elevated standards of quality nowadays demanded by the market at every level of production. In fact the quantity of the exemplars allowed, which are not up to sample, is measured in parts per million in many sectors of production. Many methodologies have been proposed to yield high quality in the industrial production lines, in order to provide surface examination and classification. This paper describes an alternative system for surface analysis based on artificial neural networks (ANNs), developed in collaboration with the Italian manufacturer Meccanotecnica Umbra S.p.A. This system was implemented and tested in order to examine three particular surfaces of mechanical seals achieving good results in comparison with the deterministic system already implemented.
quality control system mechanical front seals artificial neural networks surface fault detection surface diagnosis classification problem