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Treatment selection by constraint propagation a case study in cutting fluid selection

Published online by Cambridge University Press:  27 February 2009

James E. Mogush,
Dominique Carrega,
Peter Spirtes and
Mark S. Fox
James E. Mogush
Affiliation:
Intelligent Systems Laboratory, Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, U.S.A.
Dominique Carrega
Affiliation:
Intelligent Systems Laboratory, Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, U.S.A.
Peter Spirtes
Affiliation:
Intelligent Systems Laboratory, Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, U.S.A.
Mark S. Fox
Affiliation:
Intelligent Systems Laboratory, Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, U.S.A.
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Abstract

The GREASE project is an investigation of the application of artificial intelligence to cutting fluid selection and blending for metal machining operations. The problem is to first diagnose the machining operations to determine what fluid characteristics are required, then to select a cutting fluid which satisfies the required characteristics. The problem is exacerbated by the need to select a single fluid to be used by multiple types of operations on a variety of materials. Diagnosis is relatively simple, but treatment specification is difficult due to the variety of operations to be handled.

GREASE uses heuristic search in which the evaluation function is heuristically constructed. The construction of the evaluation function begins with the determination of the characteristics of an optimal fluid based on deep knowledge of the machining operations and materials. This is then altered heuristically according to problems diagnosed with the current fluid. Once the evaluation function is complete, it is used to select an existing fluid from the product line. GREASE has been tested extensively with results which equal that of the experts and has been field tested by the Chevron Corporation.

Information

Type
Research Article
Information
AI EDAM , Volume 2 , Issue 3 , August 1988, pp. 135 - 168
Copyright
Copyright © Cambridge University Press 1988

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