Research Papers

Hierarchical Fuzzy Primitive Surface Classification From Tessellated Solids for Defining Part-to-Part Removal Directions

[+] Author and Article Information
Nima Rafibakhsh

School of Mechanical, Industrial
and Manufacturing Engineering,
Oregon State University,
Corvallis, OR 97331
e-mail: rafibakn@oregonstate.edu

Matthew I. Campbell

School of Mechanical, Industrial
and Manufacturing Engineering,
Oregon State University,
Corvallis, OR 97331
e-mail: matt.campbell@oregonstate.edu

1Corresponding author.

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received January 31, 2017; final manuscript received September 19, 2017; published online November 28, 2017. Assoc. Editor: Francesco Ferrise.

J. Comput. Inf. Sci. Eng 18(1), 011006 (Nov 28, 2017) (12 pages) Paper No: JCISE-17-1023; doi: 10.1115/1.4038144 History: Received January 31, 2017; Revised September 19, 2017

This paper is arranged in three main sections: the first section is a hierarchical method based on clustering and a fuzzy membership system where the tessellated three-dimensional (3D) models are classified into their containing primitives: cylinder, cone, sphere, and flat. In the second section, automated assembly planning (AAP) is considered as the main application of our novel hierarchical primitive classification approach. The classified primitives obtained from the first section are used to define the removal directions between mating parts in an assembly model. Finally, a fuzzification method is used to express the uncertainty of the detected connections between every pair of parts. The acquired uncertainties are used in a user interaction process to approve, deny, or modify the connections with higher uncertainties.

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Fig. 1

Example of how primitive classification can be applied on AAP: (a) assembly model, (b) assembly components, (c) overlapping primitives, and (d) removal direction obtained by analyzing the overlapping primitives

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Fig. 2

(a) An industrial cad model and (b) a computer graphic model

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Fig. 3

Possible misclassifications in sparse and condensed clustering proposed in Ref. [2]. Sparse triangles misclassified in condensed.

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Fig. 4

Overall work flow of primitive classification

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Fig. 5

Tessellated primitives with edge information: (a) flat area, (b) cylinder, (c) cone, (d) sphere, (e) sharp-edge, and (f) flat-to-curve

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Fig. 6

Membership function of edge classification indices: (a) ABN, (b) PDC, and (c) SM

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Fig. 7

Partial set of edge classification ruleset

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Fig. 8

A tessellated model with a flat region on the left connected to a cylinder on the right

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Fig. 9

Patch determination flowchart

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Fig. 10

Example—decision making in patch determination

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Fig. 11

Results obtained from sparse and condensed clustering

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Fig. 12

Some misclassifications obtained from sparse and condensed clustering can be fixed during primitive classification process

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Fig. 13

Primitive classification results

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Fig. 14

Running time of primitive classification based on number of triangles in the models

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Fig. 15

Comparing two similar primitive classification results (a) obtained from the approach introduced by Sunil and Pande [28] and (b) our method

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Fig. 16

Adjacent blocking determination hierarchy

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Fig. 17

Fuzzification of the μ variable

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Fig. 18

Flowchart to detect overlapping between two example primitives as well as the uncertainty of the detection



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