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research-article

Automatic Detection of Fasteners from Tessellated Mechanical Assembly Models

[+] Author and Article Information
Nima Rafibakhsh

Dearborn Hall - Room 102 - Oregon State University, Corvallis OR 97330
rafibakn@oregonstate.edu

Weifeng Huang

Dearborn Hall - Room 102 - Oregon State University, Corvallis OR 97330
huangwe@oregonstate.edu

Matthew I. Campbell

Rogers Hall 304 - Oregon State University, Corvallis, OR 97331-6001
matt.campbell@oregonstate.edu

1Corresponding author.

ASME doi:10.1115/1.4038292 History: Received November 01, 2016; Revised October 18, 2017

Abstract

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 and Helix Detector, and four machine learning classifiers, Voted Perceptron, Naïve Bayes, Linear Discriminant Analysis and Gaussian Process, 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.

Copyright (c) 2017 by ASME
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