Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

A Decision-based Framework for Exploring Assembly Configuration

Published

Author(s)

Gerard Kim

Abstract

In most engineering design processes, there are three major phases of design: functional, conceptual, and detailed design (Gui94). However, most current computer-aided design (CAD) systems are oriented toward support in detailed design (e.g. geometry manipulation) only. In optimizing product assemblies, designers need to consider alternative assembly configurations at preliminary design stages and switch back and forth between the symbolic and geometric design space. This paper presents an assembly modeling framework, called CAMF, that allows, 1) an ability to create and maintain evolving assembly designs, 2) mixture of top-down and bottom-up assembly modeling, and 3) incorporation of analysis/feedback at early design stages for further design refinement. CAMF stores design alternatives at several levels of abstraction defined by generic assembly design process. Using CAMF, designers can explore different design alternatives, employ manufacturing/assembly analysis methods a different stages of design, and achieve the goals of concurrent engineering more effectively.
Proceedings Title
Proceedings of the 95 Concurrent Engineering Conference

Keywords

design alternatives, design exploration, design process

Citation

Kim, G. (1995), A Decision-based Framework for Exploring Assembly Configuration, Proceedings of the 95 Concurrent Engineering Conference (Accessed December 15, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created April 1, 1995, Updated February 17, 2017