Hostname: page-component-848d4c4894-75dct Total loading time: 0 Render date: 2024-05-03T23:46:15.323Z Has data issue: false hasContentIssue false
  • English
  • Français

The represenaion of metallurgical knowledge for alloy design

Published online by Cambridge University Press:  27 February 2009

I. Hulthage ,
M. A. Przystupa ,
M. L. Farinacci  and
M. D. Rychener
I. Hulthage
Affiliation:
Robotics Institute, Carnegie–Mellon University, Pittsburgh, PA 15213
M. A. Przystupa
Affiliation:
P.O. Box 39, Redondo Beach, CA 90277
M. L. Farinacci
Affiliation:
ALCOA Laboratories, ALCOA Center, PA 15069, U.S.A.
M. D. Rychener
Affiliation:
Robotics Institute, Carnegie–Mellon University, Pittsburgh, PA 15213
Get access Rights & Permissions [Opens in a new window]

Abstract

This article gives an overview of the metallurgical database of ALADIN, an expert system that aids metallurgists in the design of new aluminum alloys. Declarative structured representations in the form of schemata are used for metallurgical data and concepts. The representation is very general, as the goal has been to create a representation for all knowledge about aluminum alloys and metallurgy relevant to the design process. The alloy database and the architecture of the microstructure database is discussed in detail. The microstructure of alloys is described by an enumeration of the types of microstructural elements present along with their characteristics.

Type
Research Article
Information
AI EDAM , Volume 1 , Issue 3 , August 1987 , pp. 159 - 168
Copyright
Copyright © Cambridge University Press 1987

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

Boag, W. A. Jr, Reiser, D. B., Sprowls, D. O. and Rychener, M. D. 1986. CORDIAL—A knowledge-based system for the diagnosis of stress corrosion behavior in high strength aluminum alloys. In: Harrison, R. J. and Roth, L. D. (Eds.), Artificial Intelligence Applications in Materials Science. Warrendale, PA: Metallurgical Society of AIME and ASM, The Metallurgical Society Inc.Google Scholar
Brachman, R. J. 1979. On the epistomological status of semantic networks. In: Findler, N. V. (Ed.), Associative Networks. London: Academic Press.Google Scholar
Carnegie Group Inc. 1986. Knowledge Craft, Version 3.1. Pittsburgh, PA: Carnegie Group Inc.Google Scholar
Dixon, J. R. 1986. Artificial intelligence and design: a mechanical engineering view. Proceedings of the Fifth National Conference on Artificial Intelligence. Los Altos, CA: Morgan Kauffmann.Google Scholar
Farinacci, M. L., Fox, M. S., Hulthage, I. and Rychener, M. D. 1986. The development of ALADIN, an expert system for aluminum alloy design. Third International Conference on Advanced Information Technology. Amsterdam: North Holland.Google Scholar
Fox, M. S. 1979. On inheritance in knowledge representation. Proceedings of the Sixth International Joint Conference on Artificial Intelligence. Tokyo, JapanGoogle Scholar
Hornbogen, E. (1984). On the microstructure of alloys. Acta Metallurgica 32, 615.CrossRefGoogle Scholar
Hulthage, I., Rychener, M. D., Fox, M. S. and Farinacci, M. L. 1986. The use of quantitative databases in ALADIN, an alloy design system. In: J.S., Kowalik (Ed.), Coupling Symbolic and Numerical Computing in Expert Systems. Amsterdam: North-Holland.Google Scholar
Nau, D. and Gray, M. 1988. Hierarchical knowledge clustering: a way to represent and use problem-solving knowledge. In: Hendler, J. A. (Ed.), Expert Systems: The User Interface. Norwood, NJ: Ablex Publishing.Google Scholar
Requicha, A. A. G. 1980. Representations for rigid solids: theory, methods and systems. Computing Surveys 12, 437–464.CrossRefGoogle Scholar
Rychener, M. D., Farinacci, M. L., Hulthage, I. and Fox, M. S 1986. Integrating multiple knowledge sources in ALADIN, an alloy design system. Fifth National Conference on Artificial Intelligence. Philadelphia: AAAI.Google Scholar
Underwood, E. E. 1970. Quantitative Stereology. Reading, MA:Addison-Wesley.Google Scholar
Vasudevan, A. K., Ludwiczak, E. A., Baumann, R. D., Doherty, R. D. and Kersker, M. M. 1985. Fracture behavior in Al-Li alloys: role of grain boundary Δ. Materials Science Engineering 72, L25.CrossRefGoogle Scholar
Woods, W. A. 1983. What's important about knowledge representation? Computer, 16, 22–27.CrossRefGoogle Scholar