Research Papers

Automatic Detection of Fasteners From Tessellated Mechanical Assembly Models

[+] Author and Article Information
Nima Rafibakhsh

School of Mechanical, Industrial
and Manufacturing Engineering,
Oregon State University,
Dearborn Hall—Room 102,
Corvallis, OR, 97330
e-mail: rafibakn@oregonstate.edu

Weifeng Huang

School of Mechanical, Industrial
and Manufacturing Engineering,
Oregon State University,
Dearborn Hall—Room 102,
Corvallis, OR 97330 e-mail: huangwe@oregonstate.edu

Matthew I. Campbell

School of Mechanical, Industrial
and Manufacturing Engineering,
Oregon State University,
Rogers Hall 304,
Corvallis, OR 97331-6001
e-mail: matt.campbell@oregonstate.edu

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received November 1, 2016; final manuscript received October 18, 2017; published online November 28, 2017. Assoc. Editor: Bahram Ravani.

J. Comput. Inf. Sci. Eng 18(1), 011005 (Nov 28, 2017) (12 pages) Paper No: JCISE-16-2124; doi: 10.1115/1.4038292 History: Received November 01, 2016; Revised October 18, 2017

In this paper, we present multiple methods to detect fasteners (bolts, screws, and nuts) from tessellated mechanical assembly models. There is a need to detect these geometries in tessellated formats because of features that are lost during the conversions from other geometry representations to tessellation. Two geometry-based algorithms, projected thread detector (PTD) and helix detector (HD), and four machine learning classifiers, voted perceptron (VP), Naïve Bayes (NB), linear discriminant analysis, and Gaussian process (GP), are implemented to detect fasteners. These six methods are compared and contrasted to arrive at an understanding of how to best perform this detection in practice on large assemblies. Furthermore, the degree of certainty of the automatic detection is also developed and examined so that a user may be queried when the automatic detection leads to a low certainty in the classification. This certainty measure is developed with three probabilistic classifier approaches and one fuzzy logic-based method. Finally, once the fasteners are detected, the authors show how the thread angle, the number of threads, the length, and major and root diameters can be determined. All of the mentioned methods are implemented and compared in this paper. A proposed combination of methods leads to an accurate and robust approach of performing fastener detection.

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Fig. 1

(a) A tessellated bolt and (b) magnified image of the threaded part of a tessellated bolt

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Fig. 2

The overall flow of the topics discussed in the paper

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Fig. 3

(a) An assembly with 211 parts and (b) result of the small and large object clustering algorithm

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Fig. 4

Bolt/screw axis—(a) bolt, (b) OBC, and (c) central axis of the OBC and axis of the bolt

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Fig. 5

(a) A bolt and (b) 2D projected thread created from ray casting of the points generated on the surface of the bolt's OBC

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Fig. 6

Partitioning and ray casting to detect threads

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Fig. 7

(a) Threads created by SolidWorks toolbox plugin and (b) PC on the same bolt

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Fig. 8

Helix detector algorithm

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Fig. 9

Primitive classification of threads: (a) a screw and (b) its classified primitives. Flat, cylinder and cone are represented by green, red and pink, respectively.

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Fig. 10

Training data with CCAR and LOG NCC input features. The circles indicate fasteners and the stars are nonfasteners.

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Fig. 11

Predictive linear probability space with fastener training and testing data for (a) GNB, (b) LDA, and (c) GP. The circles indicate fasteners and stars represent nonfasteners.

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Fig. 12

(a) OBB and (b) tilted OBB

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Fig. 13

Fuzzification of the SC variable for PTD

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Fig. 14

Flowchart of the proposed best methods for two scenarios. scenario 1: only the detection of the fastener is important. scenario 2: When detection of the fasteners and the detailed properties of the detected fasteners are important.

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Fig. 15

Example scenario implementation on an assembly model, (a) an assembly with 28 parts including 16 bolts and two nuts, (b) a wizard to show the certainty of detections, and (c) detected fasteners

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Fig. 16

Complex industrial and mechanical geometries. (a) gear, (b) O-ring, and (c) spring.

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Fig. 17

A solid (a) and its possible bounding cylinders (b), (c), and (d)



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