Hostname: page-component-848d4c4894-m9kch Total loading time: 0 Render date: 2024-04-30T10:37:33.300Z Has data issue: false hasContentIssue false
  • English
  • Français

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.
Get access Rights & Permissions [Opens in a new window]

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.

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aikins, J. S., Kunz, J. C., Shortliffe, E. H. and Fallat, R. J. 1983. PUFF: an expert system for interpretation of pulmonary function data. Computers and Biomedical Research 16, 199–208.Google Scholar
American Society for Metals 1968. Metals Handbook, Vol. 3, 8th ed.Google Scholar
Clancey, W. J. 1984. Classification problem solving. In: Proceedings of the National Conference on Artificial Intelligence, pp. 49–55. Los Angeles: Morgan Kaufmann.Google Scholar
Duda, R. O., Hart, P. E., Barrett, P., Gashnig, J., Konolige, K., Reboh, R. and Slocum, J. 1978. Development of the PROSPECTOR Consultant System for Mineral Exploration. Final Report for SRI Projects 5821 and 6415. Technical Report, Artificial Intelligence Center, SRI International.Google Scholar
Fox, M. S., Lowenfeld, S. and Kleinosky, P. 1983. Techniques for sensor-bases diagnosis, In: Proceedings of the Eighth International Joint Conference on Artificial Intelligence, pp 158–163. Los Angeles: Morgan Kaufmann.Google Scholar
Gulf Oil 1981 a. Selection of Cutting and Grinding Fluids, Module X-Lubricants and Lubrication.Google Scholar
Gulf Oil 1981 b. Metal Machining with Cutting Fluids.Google Scholar
Gulf R–D 1982. Gulfcut Selection Guide for Machining and Grinding Operations.Google Scholar
Kahn, G. S. 1987. From application shell to knowledge acquisition system. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 355–359. Los Angeles: Morgan Kaufmann.Google Scholar
Machinability Data Center 1972. Machining Data Handbook, 2nd ed. Cincinnati: Metaut Research Assoc. Inc.Google Scholar
Mogush, J. E., Carrega, D., Spirtes, P. and Fox, M. S. 1986. Treatment as Heuritic Construction: A Case Study in Cutting Fluid Selection. Report CMU-RI-TR-86–15, Robotics Institute, Carnegie-Mellon University, Pittsburgh.Google Scholar
Osborne, R. L., 1986. Online, artificial intelligence-based turbine generator diagnostics. AI Magazine, 97–103.Google Scholar
Pan, J. Y-C. and Tenenbaum, J. M. 1986. PIES: An engineer’s do-it-yourself knowledge system for interpretation of parametric test data. AI Magazine, 62–69.Google Scholar
Shortliffe, E. H. 1976. Computer-Based Medical Consultations: MYCIN. New York: American Elsevier.Google Scholar
Versonder, G. T. et al. 1983. ACE: An expert system for telephone cable maintenance. In Proceedings of the Eighth International Joint Conference on Artificial Intelligence, pp. 116–121. Reading: Addison-Wesley.Google Scholar