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Special section: Strategies for Design Under Uncertainty

A Multidomain Engineering Change Propagation Model to Support Uncertainty Reduction and Risk Management in Design

[+] Author and Article Information
Bahram Hamraz1

Engineering Design Centre, Department of Engineering,  University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UKbh351@cam.ac.uk

Nicholas H. M. Caldwell

Engineering Design Centre, Department of Engineering,  University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UKnhmc1@cam.ac.uk

P. John Clarkson

Engineering Design Centre, Department of Engineering,  University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UKpjc10@cam.ac.uk

1

Corresponding author.

J. Mech. Des. 134(10), 100905 (Sep 28, 2012) (14 pages) doi:10.1115/1.4007397 History: Received January 30, 2012; Revised July 10, 2012; Published September 21, 2012; Online September 28, 2012

Engineering change (EC) is a source of uncertainty. While the number of changes to a design can be optimized, their existence cannot be eliminated. Each change is accompanied by intended and unintended impacts both of which might propagate and cause further knock-on changes. Such change propagation causes uncertainty in design time, cost, and quality and thus needs to be predicted and controlled. Current engineering change propagation models map the product connectivity into a single-domain network and model change propagation as spread within this network. Those models miss out most dependencies from other domains and suffer from “hidden dependencies”. This paper proposes the function-behavior-structure (FBS) linkage model, a multidomain model which combines concepts of both the function-behavior-structure model from Gero and colleagues with the change prediction method (CPM) from Clarkson and colleagues. The FBS linkage model is represented in a network and a corresponding multidomain matrix of structural, behavioral, and functional elements and their links. Change propagation is described as spread in that network using principles of graph theory. The model is applied to a diesel engine. The results show that the FBS linkage model is promising and improves current methods in several ways: The model (1) accounts explicitly for all possible dependencies between product elements, (2) allows capturing and modeling of all relevant change requests, (3) improves the understanding of why and how changes propagate, (4) is scalable to different levels of decomposition, and (5) is flexible to present the results on different levels of abstraction. All these features of the FBS linkage model can help control and counteract change propagation and reduce uncertainty and risk in design.

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Copyright © 2012 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

The CPM approach

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Figure 2

Types of knowledge

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Figure 3

FBS linkage model assumptions

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Figure 4

FBS linkage network

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Figure 6

FBS linkage network for the hairdryer

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Figure 7

(a) Component-clustered FBS linkage MDM and (b) attribute-clustered FBS linkage MDM

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Figure 8

FBS Linkage model for different levels of decomposition (Note: Only selected links are shown.)

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Figure 9

Combined risk MDM for the hairdryer (component clustered)

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Figure 10

Diesel engine – (a) photo and (b) component decomposition

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Figure 11

Diesel engine - product view of FBS Linkage model network

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Figure 12

Diesel engine – component-component DSM for the attribute Geometry including direct likelihood values [in %]

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Figure 13

Diesel engine – combined risk FBS Linkage MDM

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Figure 14

Diesel engine – collapsed combined risk DSM (Notes: Values in %; numbers below 0.5 rounded down to zero and not shown.)

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