Statistical Control of Multivariant Processes through the Artificial Neural Network Multilayer Perceptron and the MEWMA Graphic Analysis

Nowadays the supervision of the quality in the production systems is a very important task. In order to reach this goal, it is necessary that quality of the products is supervised from a global approach where all the quality characteristics are considered. In addition, it is necessary to apply multivariate statistical process control techniques to guarantee a correct control of the manufacturing systems. An important disadvantage of multivariate statistical control is the lack of a complementary analysis that identify the presence of variations or out of control signals that allow locate the source of the instability in the manufacturing process. In order to solve this problem, this research proposes using a multilayer perceptron artificial neural network as an analysis mechanism for each out-of-control signal detected by the multivariate exponentially weighted moving average control chart which is able to identify small changes in the quality of any process. The monitoring and control procedure were used in a process of bearings manufactured for the automotive industry, the results show that the mechanism detected and interpreted small variations and significant changes successfully, identifying the origin of the variation generated in the productive system with an accuracy of 99.85%.