Recognition Improvement of Control Chart Pattern Using Artificial Neural Networks

Abstract

Control charting is an important tool in SPC to improve the quality of products. Unnatural patterns in control charts assume that an assignable cause affecting the process is present and some actions must be applied to overcome the problems. An accurate recognition of control chart patterns is essential in high speed production processes where data is recorded and can be plotted in real-time. By its automatic and fast recognition ability the neural network provide the best performance to immediately recognize process trends. In this paper, a neural network model is used to control chart pattern recognition (CCPR). Several forms of architecture have been tested and the results point out an architecture which leads to excellent quality of recognition.