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Episodal associative memory approach for sequencing interactive features in process planning

Published online by Cambridge University Press:  27 February 2009

Tim E. Westhoven,
C. L. Philip Chen,
Yoh-Han Pao and
Steven R. LeClair
Tim E. Westhoven
Affiliation:
Wright Laboratory, Wright-Patterson Air Force Base, Ohio, OH 45433
C. L. Philip Chen
Affiliation:
Department of Computer Science and Engineering, Wright State University, Dayton, OH 45435
Yoh-Han Pao
Affiliation:
Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, U.S.A.
Steven R. LeClair
Affiliation:
Wright Laboratory, Wright-Patterson Air Force Base, Ohio, OH 45433
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Abstract

Process planning is the function that converts an engineering design into a manufacturing plan. One of the problems in feature-based process planning is the sequencing of features. Features must be given an order for removal. This order, or sequence, is partially dependent on the geometric relationships between the features. If the geometric relationships between features are such that they dictate a particular sequence, the features are said to have an interaction. Identifying these interactions is an important first step in creating the process plan. An approach to solve this problem using constructive solid geometry operations and the Episodal Associative Memory (EAM) is demonstrated. The EAM is an associative memory that integrates dynamic memory organization and neural computing techniques. The geometric feature relationships can be represented by a pattern. This pattern captures very qualitative information about the geometric positions fo the features. The EAM can organize these patterns into groups of similar geometric relationships. A method for dealing with exceptions, and for retrieving and storing general machining problems associated with interacting features will be described. The system implemented is shown to correctly sequence several types of feature interactions.

Information

Type
Research Article
Information
AI EDAM , Volume 6 , Issue 3 , August 1992, pp. 177 - 197
Copyright
Copyright © Cambridge University Press 1992

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