Developing a real-time pattern matching algorithm using artificial neural network for a reliable quality control in industrial applications
Yıl: 2021 Cilt: 11 Sayı: 2 Sayfa Aralığı: 537 - 546 Metin Dili: İngilizce DOI: 10.17714/gumusfenbil.826323 İndeks Tarihi: 26-05-2021
Developing a real-time pattern matching algorithm using artificial neural network for a reliable quality control in industrial applications
Öz: Today, making quality control systems with reliable accuracy is very important in producing industrial products with zerodefects. In this respect, it is an essential issue that camera control systems work with reliable control algorithms. In thisstudy, a real-time control algorithm using a pattern matching algorithm has been developed to optimize the minimumcontrast parameter with an Artificial Neural Network (ANN). In the study, the comparison of three algorithms includedin pattern matching in terms of time was made using LabVIEW image control tools. Besides, one of the most criticalparameters in the low-discrepancy sampling algorithm, which gives good results in time, minimum contrast parameter isdiscussed. The optimization of this parameter is done by using the Levenberg-Marquardt training algorithm in ANN. Theobtained results show that the proposed pattern matching algorithm using ANN for optimizing the minimum contrastparameter is fast and effective for quality control applications.
Anahtar Kelime: Endüstriyel uygulamalarda güvenilir bir kalite kontrolü için yapay sinir ağı kullanan gerçek zamanlı bir desen eşleştirme algoritmasının geliştirilmesi
Öz: Günümüzde kalite kontrol sistemlerinin güvenilir bir doğrulukta yapılması, endüstriyel ürünlerin sıfır hata ile üretimi hedefi açısından oldukça önemlidir. Bu açıdan, kameralı kontrol sistemlerinin güvenilir kontrol algoritmaları ile çalışması önemli bir konudur. Bu çalışmada, desen eşleştirme algoritmasını kullanan gerçek zamanlı bir kontrol algoritması, minimum kontrast parametresini yapay sinir ağı (YSA) ile optimize edecek şekilde geliştirilmiştir. Çalışmada örüntü eşleştirmeye dahil edilen üç algoritmanın zaman açısından karşılaştırılması LabVIEW görüntü kontrol araçları kullanılarak yapılmıştır. Ayrıca, zaman açısından iyi sonuçlar veren düşük-tutarsızlık örnekleme algoritmasında en önemli parametrelerden biri olan minimum kontrast parametresi tarışılmıştır. Bu parametrenin optimizasyonu YSA'da Levenberg-Marquardt eğitim algoritması kullanılarak yapılmıştır. Kullanılan yöntem sayesinde, desen eşleştirmesinin hızlı ve etkili olduğu görülmüştür.
Anahtar Kelime: Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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| APA | Güzelce B, Gökay BAYRAK A (2021). Developing a real-time pattern matching algorithm using artificial neural network for a reliable quality control in industrial applications. , 537 - 546. 10.17714/gumusfenbil.826323 |
| Chicago | Güzelce Burak,Gökay BAYRAK Associate Professor Developing a real-time pattern matching algorithm using artificial neural network for a reliable quality control in industrial applications. (2021): 537 - 546. 10.17714/gumusfenbil.826323 |
| MLA | Güzelce Burak,Gökay BAYRAK Associate Professor Developing a real-time pattern matching algorithm using artificial neural network for a reliable quality control in industrial applications. , 2021, ss.537 - 546. 10.17714/gumusfenbil.826323 |
| AMA | Güzelce B,Gökay BAYRAK A Developing a real-time pattern matching algorithm using artificial neural network for a reliable quality control in industrial applications. . 2021; 537 - 546. 10.17714/gumusfenbil.826323 |
| Vancouver | Güzelce B,Gökay BAYRAK A Developing a real-time pattern matching algorithm using artificial neural network for a reliable quality control in industrial applications. . 2021; 537 - 546. 10.17714/gumusfenbil.826323 |
| IEEE | Güzelce B,Gökay BAYRAK A "Developing a real-time pattern matching algorithm using artificial neural network for a reliable quality control in industrial applications." , ss.537 - 546, 2021. 10.17714/gumusfenbil.826323 |
| ISNAD | Güzelce, Burak - Gökay BAYRAK, Associate Professor. "Developing a real-time pattern matching algorithm using artificial neural network for a reliable quality control in industrial applications". (2021), 537-546. https://doi.org/10.17714/gumusfenbil.826323 |
| APA | Güzelce B, Gökay BAYRAK A (2021). Developing a real-time pattern matching algorithm using artificial neural network for a reliable quality control in industrial applications. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 11(2), 537 - 546. 10.17714/gumusfenbil.826323 |
| Chicago | Güzelce Burak,Gökay BAYRAK Associate Professor Developing a real-time pattern matching algorithm using artificial neural network for a reliable quality control in industrial applications. Gümüşhane Üniversitesi Fen Bilimleri Dergisi 11, no.2 (2021): 537 - 546. 10.17714/gumusfenbil.826323 |
| MLA | Güzelce Burak,Gökay BAYRAK Associate Professor Developing a real-time pattern matching algorithm using artificial neural network for a reliable quality control in industrial applications. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, vol.11, no.2, 2021, ss.537 - 546. 10.17714/gumusfenbil.826323 |
| AMA | Güzelce B,Gökay BAYRAK A Developing a real-time pattern matching algorithm using artificial neural network for a reliable quality control in industrial applications. Gümüşhane Üniversitesi Fen Bilimleri Dergisi. 2021; 11(2): 537 - 546. 10.17714/gumusfenbil.826323 |
| Vancouver | Güzelce B,Gökay BAYRAK A Developing a real-time pattern matching algorithm using artificial neural network for a reliable quality control in industrial applications. Gümüşhane Üniversitesi Fen Bilimleri Dergisi. 2021; 11(2): 537 - 546. 10.17714/gumusfenbil.826323 |
| IEEE | Güzelce B,Gökay BAYRAK A "Developing a real-time pattern matching algorithm using artificial neural network for a reliable quality control in industrial applications." Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 11, ss.537 - 546, 2021. 10.17714/gumusfenbil.826323 |
| ISNAD | Güzelce, Burak - Gökay BAYRAK, Associate Professor. "Developing a real-time pattern matching algorithm using artificial neural network for a reliable quality control in industrial applications". Gümüşhane Üniversitesi Fen Bilimleri Dergisi 11/2 (2021), 537-546. https://doi.org/10.17714/gumusfenbil.826323 |
