Automatic recognition system of welding seam type based on SVM method

Automatic recognition system of welding seam type based on SVM method In this paper, an automatic recognition system of welding seam type based on support vector machine (SVM) method is presented. The hardware of the proposed system consists of an industry robot with six degrees of freedom, a vision sensor, and a computer. The system has two parts including input feature vector computation and model building. In the input feature vector computation part, the depth values of a series of points of the welding joint are taken as feature vector, which are determined by four steps including main line extraction of the laser stripe, normalization of the laser stripe, selection of the left and right edge points of the welding joint, and normalization of feature vectors. In the model building part, SVM-based modeling method is used to achieve welding seam type recognition. At first, RBF kernel function is employed for classification of welding seam types. Then, the parameters of RBF are determined by a grid search method using cross-validation. After the optimal parameters of RBF being determined, the SVM model is built, and it could be used to predict welding seam type. Finally, a series of welding seam type recognition experiments are implemented. Experimental results show that the proposed system can achieve welding seam type recognition accurately and the computation cost can be reduced compared with previous methods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The International Journal of Advanced Manufacturing Technology Springer Journals

Automatic recognition system of welding seam type based on SVM method

Loading next page...
 
/lp/springer_journal/automatic-recognition-system-of-welding-seam-type-based-on-svm-method-fKtV0xEK03
Publisher
Springer London
Copyright
Copyright © 2017 by Springer-Verlag London
Subject
Engineering; Industrial and Production Engineering; Media Management; Mechanical Engineering; Computer-Aided Engineering (CAD, CAE) and Design
ISSN
0268-3768
eISSN
1433-3015
D.O.I.
10.1007/s00170-017-0202-8
Publisher site
See Article on Publisher Site

Abstract

In this paper, an automatic recognition system of welding seam type based on support vector machine (SVM) method is presented. The hardware of the proposed system consists of an industry robot with six degrees of freedom, a vision sensor, and a computer. The system has two parts including input feature vector computation and model building. In the input feature vector computation part, the depth values of a series of points of the welding joint are taken as feature vector, which are determined by four steps including main line extraction of the laser stripe, normalization of the laser stripe, selection of the left and right edge points of the welding joint, and normalization of feature vectors. In the model building part, SVM-based modeling method is used to achieve welding seam type recognition. At first, RBF kernel function is employed for classification of welding seam types. Then, the parameters of RBF are determined by a grid search method using cross-validation. After the optimal parameters of RBF being determined, the SVM model is built, and it could be used to predict welding seam type. Finally, a series of welding seam type recognition experiments are implemented. Experimental results show that the proposed system can achieve welding seam type recognition accurately and the computation cost can be reduced compared with previous methods.

Journal

The International Journal of Advanced Manufacturing TechnologySpringer Journals

Published: Mar 7, 2017

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off