Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-02T22:23:32.164Z Has data issue: false hasContentIssue false
  • English
  • Français

Knowledge-based inspection planning

Published online by Cambridge University Press:  27 February 2009

Huaming Lee ,
Jon Sims Williams  and
James Tannock
Huaming Lee
Affiliation:
Faculty of Engineering, University of Bristol, Bristol BS8 1TR, U.K.
Jon Sims Williams
Affiliation:
Faculty of Engineering, University of Bristol, Bristol BS8 1TR, U.K.
James Tannock
Affiliation:
Faculty of Engineering, University of Bristol, Bristol BS8 1TR, U.K.
Get access Rights & Permissions [Opens in a new window]

Abstract

Inspection planning is a process of reasoning about inspection activities. As a result, a sequence of inspection actions is formulated, which, when performed, will achieve the desired measurements. In manufacturing, automated inspection technologies, such as Computer-Aided Inspection (CAI) or Co-ordinate Measuring Machines (CMMs), will be facilitated by inspection planning. Inspection planning involves the following four aspects: representation of inspection features; process formalization; modeling of inspection activities; and, finally, plan synthesis. This paper discusses an approach to knowledge-based inspection planning. Accordingly, a prototype inspection planning system has been developed, which is also described in this paper.

Type
Research Article
Information
AI EDAM , Volume 6 , Issue 3 , August 1992 , pp. 149 - 162
Copyright
Copyright © Cambridge University Press 1992

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

Cox, D. R., Lee, H., Williams, J. S. and Tannock, J. 1991. Computer aided quality assessment with an intelligent inspection planning assistant. Proceedings of the 7th National Conference on Production Research, England, 454–458.Google Scholar
Fikes, R. E. and Nilsson, N. J. 1971. STRIPS: a new approach to the application of theorem proving to problem solving. Artificial Intelligence 2, 189–208.CrossRefGoogle Scholar
Finger, J. J. 1986. Exploiting Constraints in Design Synthesis. Ph.D. thesis, Stanford University, CA.Google Scholar
Ginsgerg, M. L. and Smith, D. E. 1988. Reasoning about action I: a possible worlds approach. Artificial Intelligence 35, 165–195.CrossRefGoogle Scholar
Joshi, S. and Chang, T. C. 1990. Feature extraction and feature-based approaches in the development of design interface for process planning. Journal of Intelligent Manufacturing 1, 1–15.CrossRefGoogle Scholar
Lifschits, V. 1986. On the semantics of STRIPS. Proceedings of the 1986 Workshop on Reasoning about Actions and Plans, Timberland, OR, 1–9.Google Scholar
McCarthy, J., Hays, P. J. 1969. Some philosophical problems from the standpoints of artificial Intelligence. Machine Intelligence, 4, 463–502.Google Scholar
Newell, A., Shaw, J. C. and Simon, H. A. 1960. Report on a general problem solving program for a computer. Proceedings of the International Conference Information Processing, UNESCO, Paris, 256–264.Google Scholar
Sacerdoti, E. D. 1973. Planning in a hierarchy of abstraction spaces. International Joint Conference on AI, 412–422.Google Scholar
Sacerdoti, E. D. 1975. The nonlinear nature of plans. International Joint Conference on AI, 412–422.Google Scholar
Stefik, M. 1981 a. Planning and meta-planning (MOLGEN: Part 1). Artificial Intelligence, 16, 111–140.CrossRefGoogle Scholar
Stefik, M. 1981 b. Planning and meta-planning (MOLGEN: Part 2). Artificial Intelligence, 16, 141–170.CrossRefGoogle Scholar
Tate, A. 1977. Project planning using a hierarchic nonlinear planner. Report 25 Department of Artificial Intelligence Research, University of Edinburgh, U.K.Google Scholar
Wilkins, D.E., Robinson, A.E. 1981. An interactive planning system. SRI Technical Note, 245.Google Scholar
Wilkins, D.E. 1983. Representation in a domain-independent planner. International Joint Conference on AI, 733–740.Google Scholar