Statistical data mining for efficient quality control in manufacturing

Extensive use of machines, flexible/re-configurable manufacturing and transition towards the fully automated factories call for intelligent use of information recorded during the manufacturing process. Modern manufacturing processes produce Terabytes of information during different stages of the process e.g sensor measurements, machine readings etc, and the major contributor of these big data sets are different quality control processes. In this article we will present methodology to extract valuable insight from manufacturing data. The proposed methodology is based on comparison of probabilities and extension of likelihood principles in statistics as a performance function for Genetic Algorithm.