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An Intelligent Multiagent Approach for Selection of Grinding Conditions

Rowe, W. Brian, Li, Yan, Chen, Xun and Mills, B. (1997) An Intelligent Multiagent Approach for Selection of Grinding Conditions. CIRP Annals - Manufacturing Technology, 46 (1). pp. 233-238. ISSN 0007-8506

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Abstract

The advantages of a multi-agent approach are presented for the selection of grinding conditions. The agents consist of case based reasoning, neural network reasoning and rule based reasoning. Case based reasoning is employed as the main problem-solving agent to select combinations of the grinding wheel and values of control parameters. Rule based reasoning is employed where relevant data are not available in the case base. A neural network is employed to select a grinding wheel if required. The operator makes the final decision about the wheel or the values of control parameters. The multi-agent approach combines the strengths of the different agents employed, to generate hybrid solutions and overcomes the limitations of any single approach. A blackboard method was used as the means of integrating the multi-agent system. The system works as expected and demonstrates the potential of using artificial intelligence for selection of grinding conditions, as well as the capability to develop a powerful database by learning from experience.

Item Type: Article
Subjects: T Technology > T Technology (General)
T Technology > TJ Mechanical engineering and machinery
Schools: School of Computing and Engineering
School of Computing and Engineering > Centre for Precision Technologies
School of Computing and Engineering > Centre for Precision Technologies > Advanced Machining Technology Group
School of Computing and Engineering > Diagnostic Engineering Research Centre
School of Computing and Engineering > Diagnostic Engineering Research Centre > Measurement System and Signal Processing Research Group
School of Computing and Engineering > Informatics Research Group
School of Computing and Engineering > Informatics Research Group > XML, Database and Information Retrieval Research Group
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Depositing User: Sharon Beastall
Date Deposited: 04 Mar 2011 11:42
Last Modified: 04 Mar 2011 11:42
URI: http://eprints.hud.ac.uk/id/eprint/9731

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