Endüstriyel uygulamalarda güvenilir bir kalite kontrolü için yapay sinir ağı kullanan gerçek zamanlı bir desen eşleştirme algoritmasının geliştirilmesi

Year 2021, Volume: 11 Issue: 2, 537 - 546, 15.04.2021

Burak Güzelce , Gökay Bayrak

https://doi.org/10.17714/gumusfenbil.826323

Abstract

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.

Keywords

Yapay sinir ağı, Desen eşleştirme, Piramit eşleştirme

Developing a real-time pattern matching algorithm using artificial neural network for a reliable quality control in industrial applications

Abstract

Today, making quality control systems with reliable accuracy is very important in producing industrial products with zero defects. In this respect, it is an essential issue that camera control systems work with reliable control algorithms. In this study, a real-time control algorithm using a pattern matching algorithm has been developed to optimize the minimum contrast parameter with an Artificial Neural Network (ANN). In the study, the comparison of three algorithms included in pattern matching in terms of time was made using LabVIEW image control tools. Besides, one of the most critical parameters in the low-discrepancy sampling algorithm, which gives good results in time, minimum contrast parameter is discussed. The optimization of this parameter is done by using the Levenberg-Marquardt training algorithm in ANN. The obtained results show that the proposed pattern matching algorithm using ANN for optimizing the minimum contrast parameter is fast and effective for quality control applications.

Keywords

Artificial neural network, Pattern matching, Pyramid matching

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