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Research Papers

Prediction of Assembly Variation During Early Design

[+] Author and Article Information
Zuozhi Zhao1

Global Research Center, General Electric Company, 1 Research Circle, KW-D253E Niskayuna, NY 12309zhaoz@research.ge.com

Michelle Bezdecny

Global Research Center, General Electric Company, 1 Research Circle, KW-D253E Niskayuna, NY 12309bezdecny@research.ge.com

Byungwoo Lee

Global Research Center, General Electric Company, 1 Research Circle, KW-D253E Niskayuna, NY 12309leeb@research.ge.com

Yanyan Wu

Global Research Center, General Electric Company, 1 Research Circle, KW-D253E Niskayuna, NY 12309wuy@research.ge.com

Dean Robinson

Global Research Center, General Electric Company, 1 Research Circle, KW-D253E Niskayuna, NY 12309robinsondm@research.ge.com

Lowell Bauer

Variation Risk Management, 787 Airplane & Services Integration, Boeing Company, Building 40-87.3, Column 3A3-2.4, Everett, WA 98204lwbauer@comcast.net

Mark Slagle

Variation Risk Management, 787 Airplane & Services Integration, Boeing Company, Building 40-87.3, Column 3A3-2.4, Everett, WA 98204mark.slagle@boeing.com

Duke Coleman

Variation Risk Management, 787 Airplane & Services Integration, Boeing Company, Building 40-87.3, Column 3A3-2.4, Everett, WA 98204duke.coleman@boeing.com

John Barnes

Variation Risk Management, 787 Airplane & Services Integration, Boeing Company, Building 40-87.3, Column 3A3-2.4, Everett, WA 98204john.g.barnes@boeing.com

Steve Walls

Variation Risk Management, 787 Airplane & Services Integration, Boeing Company, Building 40-87.3, Column 3A3-2.4, Everett, WA 98204stephen.a.walls@boeing.com

1

Corresponding author.

J. Comput. Inf. Sci. Eng 9(3), 031003 (Aug 19, 2009) (11 pages) doi:10.1115/1.3130795 History: Received June 15, 2007; Revised March 06, 2009; Published August 19, 2009

This paper presents the methods to move assembly variation analysis into early stages of aircraft development where critical partitioning, sourcing, and production decisions are often made for component parts that have not yet been designed. Our goal is to identify and develop variation prediction methods that can precede detailed geometric design and make estimates accurate enough to uncover major assembly risks. With this information in hand, design and/or manufacturing modifications can be made prior to major supplier and production commitments. In addition to estimation of the overall variation, the most significant contributors to assembly variation are also identified. In this paper, a generic framework for prediction of assembly variation has been developed. An efficient, top-down approach has been adopted. Instead of taking measurement everywhere, the variation analysis starts with airplane level requirements (e.g., load capabilities and orientation of horizontal/vertical stabilizers), and then assembly requirements (mainly geometric dimensioning and tolerancing callouts, quantifiable in quality control) are derived. Next the contributors to a particular assembly requirement are identified through data flow chain analysis. Finally, the major contributors are further characterized through a sensitivity study of metamodels or 3D variation analysis models. A case study of a vertical fin has been used to demonstrate the validity of the proposed framework. Multiple prediction methods have been studied and their applicability to variation analysis discussed. Simplified design simulation method and metamodel methods have been tested and the results are reported. Comparisons between methods have been made to demonstrate the flexibility of the analysis framework, as well as the utility of the prediction methods. The results of a demonstration test case study for vertical fin design were encouraging with modeling methods coming within 15% of deviation compared with the detailed design simulation.

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Copyright © 2009 by American Society of Mechanical Engineers
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Figures

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Figure 1

Circular dependency of variation prediction in early design

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Figure 3

Variation decision loop in early design

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Figure 4

Geometric variation function flow

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Figure 5

Different AYs are derived from the same RY

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Figure 6

Process capability data sources

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Figure 7

Simplified design of main torque box of the vertical fin

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Figure 8

A Monte Carlo simulation using a metamodel to predict the variation of Y

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Figure 9

A metamodel used to predict the variation of Y directly

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Figure 10

Vertical fin build plan

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Figure 11

DFC for the assembly Y of interest

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Figure 12

Simplified simulation model

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Figure 13

Compare ANN and quadratic regression on real measurement data

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