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Open AccessJournal ArticleDOI

A Knowledge-Based System for Supporting Mechanical CAD.

Joo-Heon Cha, +1 more
- 01 Jan 1994 - 
- Vol. 60, Iss: 579, pp 3625-3631
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TLDR
This paper describes both design object and design process using constraint networks which consist of constraint assemblies, constraint objects and constraint modules, and introduces the concept of functional constraint in the constraint network.
Abstract
We present a framework for building a knowledge-based system that can consistently and automatically utilize graphs, tables and formulae as a knowledge source in mechanical CAD. In this paper, we describe both design object and design process using constraint networks which consist of constraint assemblies, constraint objects and constraint modules. The design object is described by a hierarchical structure of the constraint networks, and the design process is represented by constraint propagation between them. We introduce the concept of functional constraint in the constraint network and show that the database search for design variables can be carried out automatically. The usefulness of this system is illustrated by the application to the design of a gear system.

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Citations
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Book ChapterDOI

Development of a design supporting system for press die of automobile panels

TL;DR: A methodology for systematic press die design, automatic checking of dimensions and implementation, and the rule-based design system is developed based on the product data.
Journal ArticleDOI

An integrated inference architecture for machine tools design involving complex knowledge

TL;DR: This paper describes the inference architecture of an intelligent CAD system that is to be used for the design of machine tools in which a complex and large amount of knowledge is used and demonstrates the implementation of the architecture with the result on the machining centre design.
Journal ArticleDOI

A constraint-based inference system for satisfying design constraints

TL;DR: An efficient algorithm that fulfills equality constraints through constraint satisfaction processes like variable elimination while taking into account inequality constraints and inferring production rules and reduces the load of the optimization procedure if necessary.
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