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Research Papers

Eliciting User Perceptions Using Assessment Tests Based on an Interactive Genetic Algorithm

[+] Author and Article Information
Emilie Poirson

e-mail: emilie.poirson@ec-nantes.fr

Jean-François Petiot

e-mail: jean-francois.petiot@irccyn.ec-nantes.fr
Ecole Centrale de Nantes,
LUNAM Université,
IRCCyN, UMR 6597,
Nantes, France

Ludivine Boivin

Technocentre Renault,
1 avenue du Golf,
Guyancourt, 78288, France
e-mail: ludivine.boivin@renault.com

David Blumenthal

AgroParitech / INRA / Le CNAM,
UMR 1145 GENIAL,
1 Rue des Olympiades 91300 Massy,
Massy, France
e-mail: david.blumenthal@prefmap.com

Diss=k=13i=1SPikj=151σj(vtkj-vij)2 where S is number of subjects, σj is standard deviation of variable Vj (cf. test 2), vtkj is optimization variable (coordinate of the target product of segment k on variable Vj), vij is coordinate of the ideal product of consumer i on variable Vj, Pik = 1 if consumer i belongs to segment k, 0 otherwise.

Contributed by the Design Theory and Methodology Committee of ASME for publication in the Journal of Mechanical Design. Manuscript received May 29, 2012; final manuscript received December 10, 2012; published online January 24, 2013. Assoc. Editor: Jonathan Cagan.

J. Mech. Des 135(3), 031004 (Jan 24, 2013) (16 pages) Paper No: MD-12-1288; doi: 10.1115/1.4023282 History: Received May 29, 2012; Revised December 10, 2012

To avoid failures in the marketplace, the control of the risks in product innovation and the reduction of the innovation cycles require fast and valid assessments from customers. An interactive genetic algorithm (IGA) is proposed for eliciting users' perceptions about the shape of a product, in order to stimulate creativity and to identify design trends. Interactive users' assessment tests are conducted on virtual products to capture and analyze users' responses. The IGA is interfaced with Computer Aided Design (CAD) software (CATIA V5) to create sets of parameterized designs in real time, which are presented iteratively by a graphical interface to the users for evaluation. After a description of the IGA, a study on the convergence of the IGA is presented. The convergence varies, according to the tuning parameters of the algorithm and the size of the design problem. An experiment was carried out with a set of 45 users on the application case, a dashboard, put forward by Renault. The implementation of the perceptive tests and the analysis of the results are described using hierarchical ascendant classification (HAC) and multivariate analysis. This paper shows how the results of tests using IGA can be used to elicit user perception and to detect design trends.

Copyright © 2013 by ASME
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Figures

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Fig. 1

Synoptic of the methodology and definition of the different stages

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Fig. 2

Synoptic of the IGA process [45]

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Fig. 3

Framework of the iterative user-test

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Fig. 5

Evolution of the distances dbest and daverage according to the number of generations of the IGA (automatic mode) for the set of parameters used in the dashboard application

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Fig. 4

Mapping of the number of generations required for the IGA in automatic mode depending on the number of variables and levels

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Fig. 6

Definition of the variables (V1–V5) to parameterize the geometry of the glass

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Fig. 7

Screenshot of the interface of the test 2, for the selection of the most elegant wine glass

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Fig. 11

Definition of the variables used to parameterize the geometry of the dashboard

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Fig. 8

Dendrogram of the glasses obtained by HAC

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Fig. 9

Forms of the different glasses in the three groups

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Fig. 12

Definition of the spline S by 6 control points and the 2 splines S1 and S2

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Fig. 13

Example of the final CAD model of the dashboard

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Fig. 10

Forms of the three proposed glasses, minimizing the dissatisfaction

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Fig. 15

Interface of the IGA test for the dashboard clearance study

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Fig. 16

Illustration of the combinations of variables

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Fig. 14

The dashboard CAD model in the vehicle

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Fig. 18

Example of a “clear” dashboard

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Fig. 19

Examples of representative dashboard for clearance for the four groups of subjects

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