Abstract
Abstract
Pattern recognition is an important issue in statistical process control because unnatural patterns displayed by control charts can be associated with specific causes that adversely impact on the manufacturing process. A common problem of existing approaches to control chart pattern (CCP) recognition is false classification between different types of CCP that share similar features in a real-time process-monitoring scenario, in which only limited pattern points are available for recognition. This study proposes a hybrid learning-based system that integrates neural networks and decision tree learning to overcome the classification problem in a real-time CCP recognition scheme. This hybrid system consists of three sequential modules, namely feature extraction, coarse classification, and fine classification. The coarse-classification model employs a four-layer back propagation network to detect and classify unnatural CCPs. The fine-classification module contains four decision trees used in a simple heuristic algorithm for further classifying the detected CCPs. Simulation experiments demonstrate that the false recognition problem has been effectively addressed by the proposed hybrid system. Compared with conventional control chart approaches, the proposed system has better performance in terms of recognition speed and also can accurately identify the type of unnatural CCP. Although a real-time CCP recognizer for the individual's (X) chart is the specific application presented here, the proposed hybrid methodology based on neural networks and decision trees can be applied to other control charts.