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TECHNICAL PAPERS

An Algorithmic Strategy for Automated Generation of Multicomponent Software Tools for Virtual Manufacturing

[+] Author and Article Information
Patrick N. Bless

Department of Mechanical Engineering,  University of Illinois at Urbana-Champaign, Urbana, IL, 61801bless@uiuc.edu

Shiv G. Kapoor

Department of Mechanical Engineering,  University of Illinois at Urbana-Champaign, Urbana, IL, 61801sgkapoor@uiuc.edu

Richard E. DeVor

Department of Mechanical Engineering,  University of Illinois at Urbana-Champaign, Urbana, IL, 61801redevor@uiuc.edu

Diego Klabjan

Department of Mechanical Engineering,  University of Illinois at Urbana-Champaign, Urbana, IL, 61801klabjan@uiuc.edu

J. Manuf. Sci. Eng 127(4), 866-874 (Aug 30, 2004) (9 pages) doi:10.1115/1.1954792 History: Received July 20, 2003; Revised August 30, 2004

This paper describes an algorithmic strategy to facilitate the generation of multicomponent software tools for computer-aided manufacturing (CAM) and virtual manufacturing (VM). Components that are often used to build CAM and VM applications include a wide range of domain-specific knowledge sources and also more general utility components with often very heterogeneous characteristics. The identification of a suitable and compatible set of these components is the first and arguably most important step during the development process of any CAM or VM application. This paper presents an algorithmic strategy that automates this development step by solving a time-expanded network problem, referred to as the component set identification (CSI) problem. A definition of the CSI problem, a mathematical formulation, a solution procedure, and some computational results are presented. Finally, an application to predict hole quality in drilling is used to illustrate the functionality of the proposed algorithmic strategy.

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Copyright © 2005 by American Society of Mechanical Engineers
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Figures

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Figure 1

Example problem: Directed graph of knowledge source and property nodes

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Figure 2

A deadlock example

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Figure 3

Time-expanded network

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Figure 4

Example 1: (a) Schematic of random network and (b) Optimal solution found by exact method

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Figure 5

Computational limit of exact solution procedure

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Figure 6

Example 2: (a) IP solution and (b) solution obtained by search heuristic

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Figure 7

Knowledge source and component library for drill hole-quality prediction

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Figure 8

Solution to IP-formulation-Equal emphasis on computational complexity and accuracy

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Figure 9

Solution to IP-formulation-Emphasis on computational complexity

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