Classification knowledge discovery in mold tooling test using decision tree algorithm

Authors:
Duen-Yian Yeh;Ching-Hsue Cheng;Shih-Chuan Hsiao
Affiliations:
Department of Information Management, Transworld Institute of Technology, Yunlin, Taiwan;Department of Information Management, National Yunlin University of Science & Technology, Yunlin, Taiwan;Department of Information Management, National Yunlin University of Science & Technology, Yunlin, Taiwan
Venue:
Journal of Intelligent Manufacturing
Year:
2011

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Abstract

The scale of Taiwan's mold industry was ranked the sixth in the world. But, under the global competitive pressure, Taiwan has lost its competitive advantage gradually. The only chance of Taiwan's mold industry lies in improving the competitive abilities in product research, development and design. In mold manufacturing cycle, mold tooling test plays a very important role at accelerating the speed of production. An experienced engineer can minimize the error rate of mold tooling test according to his rich experiences in parameter adjustment. However, this experience is mostly implicit without theoretical basis and its knowledge is difficult to be transmitted. Benefiting from the well development of data mining technologies, this study aimed at constructing an intelligent classification knowledge discovery system for mold tooling test based on decision tree algorithm, so as to explore and accumulate the experimental knowledge for the use of Taiwan's mold industry. This study took the only high-alloy steel manufacturer in Taiwan for case study, and performed system validation with 66 record data. The results showed the accuracy rates of prediction of training data and testing data are 97.6 and 86.9%, respectively. In addition, this study explored two classification knowledge rules and proposed concrete proposals for tooling test parameter adjustment. Moreover, this study provided two ways, rule verification and effectiveness comparison of four mining algorithms, to conduct model verification. The experimental results showed the decision tree algorithm has an excellent discriminatory power of classification and is able to provide clear and simple reference rules for decisions.