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Study and selection of grinding conditions Part 2: a hybrid intelligent system for selection of grinding conditions

Mills, B, Chen, Xun, Rowe, W. Brian and Li, Y (1999) Study and selection of grinding conditions Part 2: a hybrid intelligent system for selection of grinding conditions. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 213 (2). pp. 131-142. ISSN 0954-4054

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Abstract

A blackboard approach is presented for the selection of grinding conditions. The knowledge 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 have not yet accumulated in the case base. A neural network is employed to select a grinding wheel if required. The operator has ultimate control over the wheel or the values of control parameters selected. The blackboard approach combines the strengths of the different knowledge agents to generate hybrid solutions and overcomes the limitations of any single approach. 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 09:03
Last Modified: 04 Mar 2011 09:03
URI: http://eprints.hud.ac.uk/id/eprint/9726

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