Use of Decision-Tree Induction for Process Optimization and Knowledge Refinement of an Industrial Process

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TypeArticle
Journal titleJournal of Artificial Intelligence for Engineering Design
VolumeAnalysis and Manufacturing
IssueAI EDAM
Subjectdecision-tree induction; process optimization; optimisation du processus
AbstractDevelopment of expert systems involves knowledge acquisition which can be supported by applying machine learning techniques. This paper presents the basic idea of using decision-tree induction in process optimization and development of the domain model of electrochemical machining (ECM). It further discusses how decision-tree induction is used to build and refine the knowledge base of the process. The idea of developing an intelligent supervisory system with a learning component (IMAFO, Intelligent MAnufacturing FOreman) that is already implemented is briefly introduced. The results of applying IMAFO for analyzing data form 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.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number35070
NPARC number9145781
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Record identifier2a3ba591-edfe-4d5c-ace9-bc4e67c5441d
Record created2009-06-25
Record modified2016-05-09
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