Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/229
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Barstuğan, Mücahid | - |
dc.contributor.author | Ceran, Yavuz Selim | - |
dc.contributor.author | Yılmaz, Musa | - |
dc.contributor.author | Dündar, Niyazi Adem | - |
dc.date.accessioned | 2021-12-13T10:23:53Z | - |
dc.date.available | 2021-12-13T10:23:53Z | - |
dc.date.issued | 2020 | - |
dc.identifier.issn | 1757-8981 | - |
dc.identifier.uri | https://doi.org/10.1088/1757-899X/895/1/012012 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/229 | - |
dc.description | 11th International Conference on Mechatronics and Manufacturing (ICMM) -- JAN 12, 2020 -- Chuo Univ, Tama Campus, Tokyo, JAPAN | en_US |
dc.description.abstract | This study classified the defects that were encountered on single-bead welding, which was made by MIG/MAG welding machines in Hursan Press company. 2250 images were taken on weldings that were made by five different welders. This study classified defects into three classes as good welding, porosity, and discontinuities. The images in the dataset, which have three classes, were classified by two stages. In the first stage, the texture features of the welding area were extracted. In the second stage, Artificial Neural Networks (ANN) method classified the extracted features. Sensitivity, specificity, accuracy, precision, and F-score metrics were used to measure the classification performance. 1500 images were used to train the system and training and validation performances were obtained as 94.03% and 94.19%, respectively. 750 images were used to test the performance of the proposed method and the test performance was obtained as 94.31%. The proposed method detected a defect on single-bead welding in 0.98 seconds. | en_US |
dc.description.sponsorship | Hursan Press Company; Konya KOSGEB branch | en_US |
dc.description.sponsorship | This study was funded by Hursan Press Company and Konya KOSGEB branch. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IOP PUBLISHING LTD | en_US |
dc.relation.ispartof | 2020 11TH INTERNATIONAL CONFERENCE ON MECHATRONICS AND MANUFACTURING (ICMM 2020) | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | CLASSIFICATION | en_US |
dc.title | Detection of Defects on Single-Bead Welding by Machine Learning Methods | en_US |
dc.type | Conference Object | en_US |
dc.identifier.doi | 10.1088/1757-899X/895/1/012012 | - |
dc.identifier.scopus | 2-s2.0-85090115326 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
dc.identifier.volume | 895 | en_US |
dc.identifier.wos | WOS:000615716700012 | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57200139642 | - |
dc.authorscopusid | 57218705951 | - |
dc.authorscopusid | 57218708283 | - |
dc.authorscopusid | 57218705469 | - |
dc.identifier.scopusquality | - | - |
item.grantfulltext | open | - |
item.fulltext | With Fulltext | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairetype | Conference Object | - |
crisitem.author.dept | 02.04. Department of Electrical and Electronics Engineering | - |
Appears in Collections: | Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
Files in This Item:
File | Size | Format | |
---|---|---|---|
Barstugan_2020_IOP_Conf._Ser.__Mater._Sci._Eng._895_012012.pdf | 842.36 kB | Adobe PDF | View/Open |
CORE Recommender
SCOPUSTM
Citations
2
checked on May 4, 2024
WEB OF SCIENCETM
Citations
1
checked on May 4, 2024
Page view(s)
80
checked on May 6, 2024
Download(s)
62
checked on May 6, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.