Makine Öğrenmesi ve Derin Öğrenme Algoritmaları ile Baskı Devre Kartlarındaki Kusurların Tespiti

Baskı devre kartları (PCB), elektronik bileşenleri bir arada tutan ve bu bileşenler arasındaki elektrik bağlantısını sağlayan elektronik devre kartlarıdır. Baskı devre kartları, dayanıklılık, daha az ısınma, minimum kablo kullanımı ve montaj kolaylığı gibi geleneksel kablolu devrelere göre birçok avantaj sunmaktadır. Baskı devre kartlarının doğru tasarımı ve üretimi baskı devre kartlarının kalitesini ve verimliliğini önemli ölçüde etkilemektedir. Bu çalışmada baskı devre kartlarının doğru bir şekilde üretilmesine ve hata oranının en aza indirilmesine yardımcı olmak için makine öğrenmesi ve derin öğrenme algoritmalarına dayalı kusur tespit sistemi önerilmiştir. Önerilen sistemde baskı devre üzerinde yer alan eksik delik, fare ısırığı, açık devre, kısa devre, çıkıntı ve sahte bakır kusurları tespit edilmiştir. Elde edilen sonuçlara göre YOLO-v4 ile %74.62, HOG+SVM ile %47.83, HOG+KNN ile %39.86 başarı doğrulukları elde edilmiştir. Çalışmada ele alınan algoritmaların baskı devre kartlarında kusur tespitinde uygulanabilir olduğu görülmüştür.

Anahtar Kelimeler:

Yapay zeka, Makine öğrenmesi, Derin öğrenme, Baskı devre kartı.

Detection of Defects in Printed Circuit Boards with Machine Learning and Deep Learning Algorithms

Printed Circuit Boards (PCBs) are electronic boards that hold electronic components together and provide the electrical connection between these components. Printed circuit boards offer many advantages over traditional wired circuits, such as durability, less heat, minimal wiring, and ease of assembly. Correct design and production of printed circuit boards significantly affect the quality and efficiency of printed circuit boards. In this study, a defect detection system based on machine learning and deep learning algorithms is proposed to help produce printed circuit boards accurately and minimize the error rate. In the proposed system, missing hole, mouse bite, open circuit, short, spur, and spurious copper defects on the printed circuit have been determined. According to the results obtained, According to the results obtained, success accuracies of 74.62% were obtained with YOLO-v4, 47.83% with HOG+SVM, and 39.86% with HOG+KNN. It has been seen that the algorithms discussed in the study are applicable in the detection of defects in printed circuit boards.

Keywords:

Artificial intelligence, Machine learning, Deep learning, Printed circuit board,

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