Use Of Artificial Neural Networks For The Correction Of Faults On The Detected Weld Seam Image

Simple item record

Full item record

dc.contributor.authorYildirim, Şahin
dc.contributor.authorUlu, Burak
dc.contributor.organizationErciyes Universityen
dc.date.accessioned2017-12-19T12:14:19Z
dc.date.available2017-12-19T12:14:19Z
dc.date.issued2017
dc.description.abstract"Recently, the use of industrial robots has become widespread and the number of researches on this area has also increased considerably. Particularly the work done on robotic welding process is striking. Much of this work is about improving welding quality and automatically determining the welding path. Generally, 2D and 3D image processing methods have been used to determine the weld seam with various algorithms. Previous studies show that the biggest problem in this methods applied is the clear separation of the weld seam from the image. In this experimental study, artificial neural networks are used as a solution for this common problem. The most accurate result are obtained with the applied algorithms. It can be seen that the obtained results show the weld seam can be detected clearly using artificial neural networks."en
dc.identifier.citationYildirim Ş., Ulu B., Use Of Artificial Neural Networks For The Correction Of Faults On The Detected Weld Seam Image, [in:] Journal of Current Construction Issues. CIVIL ENGINEERING PRESENT PROBLEMS, INNOVATIVE SOLUTIONS - Optimization in Business and Engineering, ed. Jarosław Górecki, BGJ Consulting, 2017en
dc.identifier.isbn978-83-87480-05-9
dc.identifier.urihttps://open.icm.edu.pl/handle/123456789/13567
dc.language.isoen
dc.publisherBGJ Consultingen
dc.rightsUznanie autorstwa-Użycie niekomercyjne-Bez utworów zależnych 3.0 Polska*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/pl/*
dc.subjectwelding seam detectionen
dc.subjectneural networken
dc.subjectindustrial robotsen
dc.titleUse Of Artificial Neural Networks For The Correction Of Faults On The Detected Weld Seam Imageen
dc.typearticleen
Files for this record
Original bundle
Now showing 1 - 1 of 1
Name: 21.pdf
Size: 2.15 MB
Format: Adobe Portable Document Format
Description:
Download
License files
Name: license.txt
Size: 481 B
Format: Item-specific license agreed upon to submission
Description:
Download
Name: license_rdf
Size: 810 B
Format: RDF serialized in XML
Description:
Download
Belongs to collection
Artykuły / Articles