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Use of decision-tree induction for process optimization and knowledge refinement of an industrial process

Published online by Cambridge University Press:  27 February 2009

A. Famili
A. Famili
Affiliation:
Knowledge Systems Laboratory, Institute for Information Technology, National Research Council Canada, Ottawa, Ontario, Canada K1A 0R6
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Abstract

Development of expert systems involves knowledge acquisition that can be supported by applying machine learning techniques. The basic idea of using decision-tree induction in process optimization and development of the domain model of electrochemical machining (ECM) is presented. How decision-tree induction is used to build and refine the knowledge base of the process is also discussed.

The idea of developing an intelligent supervisory system with a learning component [Intelligent MAnufacturing FOreman (IMAFO)] that is already implemented is briefly introduced. The results of applying IMAFO for analyzing data from the ECM process are presented. How the domain model of the process (electrochemical machining) is built from the initial known information, and how the results of decision-tree induction can be used to optimize the model of the process and further refine the knowledge base are shown. Two examples are given to demonstrate how new rules (to be included in the knowledge base of an expert system) are generated from the rules induced by IMAFO. The procedure to refine these types of rules is also explained.

Keywords

decision-tree inductionprocess optimization
Type
Research Article
Information
AI EDAM , Volume 8 , Issue 1 , Winter 1994 , pp. 63 - 75
Copyright
Copyright © Cambridge University Press 1994

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