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Proceedings Paper

Intelligent nesting system on 2-D highly irregular resources
Author(s): Jason Chung; David R. Scott; Donald J. Hillman
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Paper Abstract

The objective of the automatic nesting problem is to find an arrangement for cutting irregularly shaped pieces that efficiently and quickly utilizes an available resource. The available resource, in this case, is highly irregular with not only irregular boundaries but also defective areas. In the solution of this problem, three subproblems must be considered: irregular resource/piece representation, search/match methods and localization of the search space. We propose an object-oriented representational scheme which describes pieces and resources in multiple ways. This object-oriented representational scheme can directly depict physical objects and mental concepts in a coherent manner, making it much easier to embed an expert’s knowledge into a system. We also present a novel hybrid search algorithm, an abstract heuristic search in combination with a best-first search using limited backtracking. A new way of localizing the search space called recursive look ahead is also introduced. This technique is a domain-dependent approach that sub-divides the world based on the locations of defects. This recursive look ahead procedure is a novel variant of the sub-goal technique. Several other inference procedures, such as intersection and overlap detection, try-to-fit procedures are also proposed. This system has been developed and the preliminary result is satisfactory. The testing has been performed by comparing the system against a human expert. The average yield difference has been within five percent.

Paper Details

Date Published: 1 January 1990
PDF: 12 pages
Proc. SPIE 1293, Applications of Artificial Intelligence VIII, (1 January 1990); doi: 10.1117/12.21096
Show Author Affiliations
Jason Chung, Lehigh Univ. (United States)
David R. Scott, Lehigh Univ. (United States)
Donald J. Hillman, Lehigh Univ. (United States)


Published in SPIE Proceedings Vol. 1293:
Applications of Artificial Intelligence VIII
Mohan M. Trivedi, Editor(s)

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