Research Papers

Direct Geometry Processing for Telefabrication

[+] Author and Article Information
Yong Chen

Industrial and Systems Engineering,
University of Southern California,
Los Angeles, CA 90089
e-mail: yongchen@usc.edu

Kang Li

e-mail: kli@iit.edu

Xiaoping Qian

Mechanical, Materials and
Aerospace Engineering,
Illinois Institute of Technology,
Chicago, IL 60616
e-mail: qian@iit.edu

Contributed by the Computers and Information Division of ASME for publication in the Journal of Computers and Information Science IN Engineering. Manuscript received November 2, 2012; final manuscript received May 29, 2013; published online August 19, 2013. Editor: Bahram Ravani.

J. Comput. Inf. Sci. Eng 13(4), 041002 (Aug 19, 2013) (15 pages) Paper No: JCISE-12-1199; doi: 10.1115/1.4024912 History: Received November 02, 2012; Revised May 29, 2013

This paper presents a new approach for telefabrication where a physical object is scanned in one location and fabricated in another location. This approach integrates three-dimensional (3D) scanning, geometric processing of scanned data, and additive manufacturing (AM) technologies. In this paper, we focus on a set of direct geometric processing techniques that enable the telefabrication. In this approach, 3D scan data are directly sliced into layer-wise contours. Sacrificial supports are generated directly from the contours and digital mask images of the objects and the supports for stereolithography apparatus (SLA) processes are then automatically generated. The salient feature of this approach is that it does not involve any intermediate geometric models such as STL, polygons, or nonuniform rational B-splines (NURBS) that are otherwise commonly used in prevalent approaches. The experimental results on a set of objects fabricated on several SLA machines confirm the effectiveness of the approach in faithfully telefabricating physical objects.

Copyright © 2013 by ASME
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Fig. 4

The six scanning steps with 60-deg step angle

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Fig. 3

A digitization system and software system

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Fig. 2

Direct geometric processing for telefabrication. The physical part in (a) is scanned in Chicago and the scanned data cloud is shown in (b). The data cloud is then sliced as shown in (c). Upon transferring the data to Los Angeles, support structures (d) are automatically generated from the sliced model and a physical part is built as shown in (e).

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Fig. 1

Overview of telefabrication: a physical object is scanned in one location and fabricated in another location. The scan data are processed according to the given machine specifications from micro- to mesoscales.

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Fig. 5

Overview of the Morse complex based point-cloud slicing procedure. (a) point cloud. (b) Morse function on the MLS surface. (c) critical point generation. (d) Morse-Smale complex. (e) enhanced Reeb graph. (f) sliced model. The magenta, green, and yellow and white Dots represent the maximum, (top and bottom) saddle, and minimum critical points, respectively.

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Fig. 6

Critical points and slicing topology change

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Fig. 11

Marching cube based MLS surface slicing. (a) slicing plane (yellow) and points (red) close to the plane; (b) front view; (c) marching cube grid on the slicing plane; (d) zoom- in view; (e) g(x) field; (f) three extracted zero-value contours; (g) zoom-in view only the middle contour (green, thick) is taken as the slicing contour; (h) output contour for the current slicing plane.

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Fig. 7

Morse-Smale complex and enhanced Reeb graph of a mechanical part. (a) Morse-Smale complex with ascending and descending integral lines; (b) enhanced Reeb graph extracted from the complex.

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Fig. 9

Identify the slice’s topology by intersecting the slicing plane with the enhanced Reeb graph. (a) and (d) slicing planes; (b) and (e) intersecting with the enhanced Reeb graph; (c) and (f) adaptive contour marching.

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Fig. 10

Adaptive contour marching. (a) intersection-based marching with adaptive step length; (b) step length determined by osculating circle radius; (c) partial point cloud near the slicing plane; and (d) contour points generated by marching with curvature-adaptive step lengths.

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Fig. 13

An illustration of the angle-based support generation method

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Fig. 14

An illustration of various geometries and related supports. (a) Cantilever; (b) vaulted overhang; and (c) small overhang.

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Fig. 15

An illustration of the contour-based support generation method. (a) Given layers and (b) layer analysis result.

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Fig. 16

An example of layer 107. (a) Previous layer; (b) current layer; and (c) computing result.

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Fig. 12

Slicing with topological guarantee. (a) another slicing plane where two contours are expected; (b) zoom-in view; (c) scalar field; (d) six contours extracted; (e) contours zoom-in; (f) two contours picked as actual slice contours.

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Fig. 17

An example of support layout based on region covering. (a) Input regions to be supported; (b) computed support layouts; (c) CAD model of generated supports; (d) CAD model of generated supports by the Lightyear system.

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Fig. 18

Telefabrication test 1: fertility model. (a) Scanned point-cloud data; (b) critical points and Morse complex; (c) enhanced Reeb graph; (d) sliced model; (e) built part.

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Fig. 19

Telefabrication test 2: sculpture model at original orientation. (a) Scanned point-cloud data; (b) sliced model; (c) generated supports; and (d) display of both point clouds and supports.

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Fig. 20

Test 2: sculpture model at a different orientation with fabrication machine A (layer thickness = 0.01 mm). (a) Scanned point-cloud data; (b) sliced model; (c) generated supports; and (d) built part with attached supports structure.

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Fig. 21

Raw scan data with outlier removed. (a) Six step-scan range images; (b) Six registered triangle meshes; and (c) raw scan data

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Fig. 22

Geometric data flow in the intermediate steps of telefabrication.

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Fig. 23

Face features comparison of sliced model and built part for two preprocessed slicing input.

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Fig. 24

Comparison between physical object and built part 1




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