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Research Papers

Feature Edge Extraction Via Angle-Based Edge Collapsing and Recovery

[+] Author and Article Information
Soji Yamakawa

The Department of Mechanical Engineering,
Carnegie Mellon University,
5000 Forbes Avenue,
Pittsburgh, PA 15213
e-mail: soji@andrew.cmu.edu

Kenji Shimada

The Department of Mechanical Engineering,
Carnegie Mellon University,
5000 Forbes Avenue,
Pittsburgh, PA 15213
e-mail: shimada@cmu.edu

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received October 24, 2016; final manuscript received June 20, 2017; published online January 31, 2018. Assoc. Editor: Yong Chen.

J. Comput. Inf. Sci. Eng 18(2), 021001 (Jan 31, 2018) (18 pages) Paper No: JCISE-16-2111; doi: 10.1115/1.4037227 History: Received October 24, 2016; Revised June 20, 2017

This paper presents a new method for extracting feature edges from computer-aided design (CAD)-generated triangulations. The major advantage of this method is that it tends to extract feature edges along the centroids of the fillets rather than along the edges where fillets are connected to nonfillet surfaces. Typical industrial models include very small-radius fillets between relatively large surfaces. While some of those fillets are necessary for certain types of analyses, many of them are irrelevant for many other types of applications. Narrow fillets are unnecessary details for those applications and cause numerous problems in the downstream processes. One solution to the small-radius fillet problem is to divide the fillets along the centroid and then merge each fragment of the fillet with nonfillet surfaces. The proposed method can find such fillet centroids and can substantially reduce the adverse effects of such small-radius fillets. The method takes a triangulated geometry as input and first simplifies the model so that small-radius, or “small,” fillets are collapsed into line segments. The simplification is based on the normal errors and therefore is scale-independent. It is particularly effective for a shape that is a mix of small and large features. Then, the method creates segmentation in the simplified geometry, which is then transformed back to the original shape while maintaining the segmentation information. The groups of triangles are expanded by applying a region-growing technique to cover all triangles. The feature edges are finally extracted along the boundaries between the groups of triangles.

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References

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Figures

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Fig. 1

Sample feature-edge extraction by the proposed method: (a) input triangulation without segmentation and/or feature-edge information, (b) feature edges that conventional methods are targeting, (c) simplified triangulation, and (d) feature edges extracted by the proposed method

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Fig. 2

Overview of the proposed method

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Fig. 3

2D angle-based simplification: (a) original line segments, (b) desired segmentation, and (c) simplification in which the boundaries of the segments are sharp corners

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Fig. 4

Applying λ(a⇒b) deletes edge eab and changes eac into ebc (eac is essentially rotated by θ)

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Fig. 5

Angle-based simplification tends to collapse a filleted part into a point

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Fig. 6

λ(a⇒b) will join edges eac and ebd and violate condition (iv)

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Fig. 7

Condition (v) protects a stair-step shape

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Fig. 8

Condition (5) preserving a stair-step pattern over a triangular prism shape connected to a larger surface

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Fig. 9

Collapsing a vertex in the feature-edge direction may deform but keep feature edges: (a) thick lines are feature edges, (b) Applying λ(ac) and λ(bd) would lose a large dihedral-angle edge eab and is therefore prevented by conditions (6) and (7), and (c) Applying λ(ab) (or λ(ae)) and λ(cd) (or λ(cf)) deforms but keeps the feature edges

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Fig. 10

Edge uncollapsing and face-label assignments (recovered triangles are shaded)

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Fig. 11

Edge uncollapsing and face-label assignments (recovered triangles are shaded)

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Fig. 12

A mounting bracket: (a) input triangulation, (b) simplification, (c) segmentation, and (d) close-up look of the segmentation (Downloaded from https://grabcad.com/library/ge-aero-jet-mounting-1. The model has been created by Andre Hendrikz)

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Fig. 13

Engine cover model: The simplification has some self-intersections, which does not adversely affect the outcome: (a) input triangulation, (b) simplification, and (c) segmentation

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Fig. 14

Telephone handset case: (a) input triangulation, (b) fillets around the speaker holes, (c) simplification, and (d) segmentation

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Fig. 15

A lens: (a) input triangulation, (b) simplified, (c) segmentation after uncollapsing and region growing, and (d) close-up look of extracted feature edges

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Fig. 16

Output from a uniform mesh

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Fig. 17

A cell phone case: (a) input triangulation, (b) simplification, (c) segmentation, and (d) feature edges extracted on the fillet centroids

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Fig. 18

Segmentation disrupted near bad input triangulation: cross section on the right shows large dihedral-angle edges where the original CAD model was smooth: (a) and (b) disruption of the segmentation

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Fig. 19

Gear casing model (Downloaded from https://grabcad.com/library/gear-casing-2. The model has been created by Greg Pavlik)

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Fig. 20

Segmentation with the first set of parameters: (a) simplification and (b) segmentation

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Fig. 21

Simplification and segmentation with the second attempt: (a) simplification and (b) segmentation

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Fig. 22

Simplification and segmentation with the third attempt: (a) simplification and (b) segmentation

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Fig. 23

Segmentation and simplification with the fourth attempt: (a) simplification and (b) segmentation: arrows point to unwanted narrowly separated feature edges

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Fig. 24

Filleted stairstep: (a) a stairstep filleted with relatively large radii, (b) two fillets separated by a narrow nonfillet surface, and (c) two directly connected fillets

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