Abstract
Article history: Received 30 April 2011 Received in revised form September, 01, 2011 Accepted 01 September 2011 Available online 2 September 2011 Control chart pattern (CCP) recognition can act as a problem identification tool in any manufacturing organization. Feature-based rules in the form of decision trees have become quite popular in recent years for CCP recognition. This is because the practitioners can clearly understand how a particular pattern has been identified by the use of relevant shape features. Moreover, since the extracted features represent the main characteristics of the original data in a condensed form, it can also facilitate efficient pattern recognition. The reported feature-based decision trees can recognize eight types of CCPs using extracted values of seven shape features. In this paper, a different set of seven most useful features is presented that can recognize nine main CCPs, including mixture pattern. Based on these features, decision trees are developed using CART (classification and regression tree) and QUEST (quick unbiased efficient statistical tree) algorithms. The relative performance of the CART and QUEST-based decision trees are extensively studied using simulated pattern data. The results show that the CART-based decision trees result in better recognition performance but lesser consistency, whereas, the QUEST-based decision trees give better consistency but lesser recognition performance. © 2012 Growing Science Ltd. All rights reserved
Highlights
In order to compete in global economy, every manufacturer tries to produce high quality products than their competitors
The results show that the classification and regression tree (CART)-based decision trees result in better recognition performance but lesser consistency, whereas, the QUEST-based decision trees give better consistency but lesser recognition performance
It is observed that the overall mean percentage of correct recognition achieved by the CART-based recognizers at the training and recall phases (95.53% and 94.67% respectively) are higher than those obtained by the QUEST-based recognizers (93.44% and 92.76% respectively)
Summary
In order to compete in global economy, every manufacturer tries to produce high quality products than their competitors. There is a need for developing feature-based heuristics that can recognize all the nine main types of CCPs, including MIX pattern. In this paper, using the CART-based systematic approach (Gauri and Chakraborty, 2009), a new set of seven most appropriate shape features is selected that can recognize all the nine main types of CCPs, including MIX pattern. Using these selected features, heuristic rules in the form of decision trees are developed using CART as well as QUEST algorithms for recognition of various CCPs. the relative performance of these two algorithms are extensively studied using synthetic pattern data
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