Scan Planning Strategy for a General Digitized Surface

[+] Author and Article Information
Charyar Mehdi-Souzani

 Lurpa, ENS de Cachan, 61 Av du Président Wilson, 94235 Cachan Cedex, France

François Thiébaut

 Lurpa,-ENS de Cachan, 61 Av du Président Wilson, 94235 Cachan Cedex, France and  IUT de Cachan, 9 Av Division Leclerc, 94234 Cachan Cedex, France

Claire Lartigue1

 Lurpa,-ENS de Cachan, 61 Av du Président Wilson, 94235 Cachan Cedex, France and  IUT de Cachan, 9 Av Division Leclerc, 94234 Cachan Cedex, Francelartigue@lurpa.ens-cachan.fr


Corresponding author.

J. Comput. Inf. Sci. Eng 6(4), 331-339 (Jul 06, 2006) (9 pages) doi:10.1115/1.2353853 History: Revised July 06, 2006; Received September 15, 2006

As new functional requirements of products lead to the definition of more complicated shapes, reverse engineering is playing a more important role. The process consists in defining a CAD model of the object surfaces from the measurement of the real object. Reverse engineering takes advantage of new advances in noncontact measuring systems leading to a representation of the surfaces as large clouds of points. Nevertheless, scanning without path planning may affect completeness and accuracy of the measured data. This paper addresses the problem of intelligent scan planning within the context of reverse engineering. A measuring system allows us to acquire a cloud of points, which represents the first measurement of the free-form object. This incomplete and locally inaccurate cloud of points is used as a basis to generate an intelligent scan planning. A pretreatment of the point cloud is performed to determine the quality of the first scan and to find out the characteristic edges. The method relies on a voxel representation of the data. According to given thresholds of quality criteria (noise and completeness), unsatisfactory quality zones and digitizing gaps are identified. The new scan paths for an optimal digitizing are then calculated including optimal orientation search. An experimental application of the presented work is described through the digitizing of a face mask.

Copyright © 2006 by American Society of Mechanical Engineers
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Figure 1

Digitizing system

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Figure 2

Sensor parameter positioning

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Figure 3

δ noise in function of the positioning parameters

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Figure 4

Overview of the approach

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Figure 5

First scanning of the face mask

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Figure 6

Voxel representation of the scanned mask

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Figure 7

Voxel map of noise

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Figure 8

Completeness of the data for the face mask

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Figure 9

Identification of the characteristic points

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Figure 10

Characteristic points of type 1

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Figure 11

Representation of the geometric model

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Figure 12

Representation of the voxel

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Figure 13

Representation of successive contour voxels

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Figure 14

Result of the intelligent scanning




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