Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/3774
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dc.contributor.authorKorkmaz, Mehmet-
dc.contributor.authorBarstugan, Mücahid-
dc.date.accessioned2023-03-03T13:35:01Z-
dc.date.available2023-03-03T13:35:01Z-
dc.date.issued2020-
dc.identifier.issn2148-2683-
dc.identifier.urihttps://doi.org/10.31590/ejosat.804744-
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1135939-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/3774-
dc.description.abstractThe study at hand is an implementation of a deep learning strategy on a quality control scheme. The quality control process is a substantial part of product manufacturing. It fundamentally targets to detect and eliminate defective products so that the erroneous ones will not be delivered to the customers. Final product control has been usually performed by experts. Generally, those experts can easily distinguish defective and trouble-free products. On the other hand, growing product lines and human-based natural problems may affect the efficiency of that quality control process. Therefore, there is an increasing demand for computer-aided software that will take the place of those experts. This software or algorithm typically increases the product control rate. Besides, they make it possible to avoid from human-driven faults. The algorithms run at high speed and efficacy under conditional situations i.e. perfectly lightening environment. However, they may easily fail when small changes occur in the environment or the product for some duties that humans can easily achieve. These robustness problems make them not preferable, although they have numerous advantages. At this point, deep learning-based artificial intelligence algorithms have made a significant enhancement. The general development and achievable prices of GPUs pave the way for using numerous training examples so that better networks, meaning more robust, can be created for the applications. To this end, we carried on an experiment that could realize the deep learning strategy on the quality control scheme. For this purpose, the developed algorithms applied to the inverters conveying on a product line to confirm whether they are erroneous or not. Results show that developed strategy could detect defective products similar to the human being.en_US
dc.language.isoenen_US
dc.relation.ispartofAvrupa Bilim ve Teknoloji Dergisien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAutomationen_US
dc.subjectCNNen_US
dc.subjectDeep Learningen_US
dc.subjectRobotic Armen_US
dc.subjectQuality Control Derin Öğrenmeen_US
dc.subjectKalite Kontrolen_US
dc.subjectOtomasyonen_US
dc.subjectRobot Kolen_US
dc.titleA Deep Learning-Based Quality Control Applicationen_US
dc.typeArticleen_US
dc.identifier.doi10.31590/ejosat.804744-
dc.departmentKATÜNen_US
dc.identifier.volume0en_US
dc.identifier.issueEjosat Özel Sayı 2020 (ICCEES)en_US
dc.identifier.startpage332en_US
dc.identifier.endpage336en_US
dc.institutionauthor-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanen_US
dc.identifier.trdizinid1135939en_US
item.openairetypeArticle-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
Appears in Collections:TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collections
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