A NEW APPROACH OF USING DATA MINING TOOLS FOR REDUCING COSTS OF QUALITY IN MEASURING CHEMICAL PARAMETERS

In today's competitive markets, the quality control issues have been attracted by the manufacturers more than before. In this regard, one of the most important issues that can be notable in this domain is the quality costs. Producers and manufacturers always face a two way choice of high quality of products and high related costs and should analyze these two choices to select the optimum alternative. Preparing products with high quality leads to high amounts of costs and manufacturers are always engaged in solving his problem to balance between these two issues: high quality and high amounts of related costs. In this paper we introduce data mining techniques as eective tools that can be used eectively to overcome the above problem and propose a new approach of using these techniques that can be used for the above objectives. Accordingly, we implement the data mining tools in an electrode position paint line to show the eectiveness of such tools to reduce the costs. Actually, the main objective is to extract the knowledge that can be used in reducing the number of product sampling. The data analysis is performed on the chemical parameters of this electrode position paint line which are used to control the process of painting. To obtain the above objectives, the association rule mining technique is used. The results show that we can reduce the number of parameters which are necessary to measure regarding the rules obtained by association rule mining. It is notable that the number of sampling times is reduced in such a way that the quality of the products preserves and the risk of products failures is not also increased. In other words, the costs of sampling are reduced while preserving the quality. The proposed method can be used in similar cases for reducing the costs. It is also useful for dimension reduction purpose.