Gerçek Zamanlı Yüzey Görüntüleme ve Veri Seti Oluşturma için Prototip Konveyör Bandının Tasarımı ve Üretimi

Abstract

Bu çalışmada, katı alüminyum iletkenlerin üretim süreci sırasında meydana gelebilecek yüzey deformasyonlarının görüntülenerek veri seti oluşturulmasına yönelik, konveyör bant tabanlı bir prototip sistem geliştirilmiştir. Sistem, kontrollü aydınlatma ve ayarlanabilir görüş açısına sahip kapalı bir kutuya entegre edilmiş, saniyede 60 kare hızında 1280x1024 piksel çözünürlükte görüntü alabilen yüksek çözünürlüklü bir kamera kullanılarak çalışmaktadır. Görüntüler, sabit hızda ilerleyen konveyör bandı üzerinde toplanmış ve bu sayede görüntü kalitesinde süreklilik sağlanmıştır. Geliştirilen prototip ile toplamda 1.600 görüntü toplanmış ve bu görüntüler, üç farklı kusur tipine ait olacak şekilde manuel olarak etiketlenmiştir. Elde edilen veri seti, ilerleyen aşamalarda çizik, sıyrık, ezilme gibi yüzey kusurlarının derin öğrenme algoritmaları ile tespit ve sınıflandırılmasına olanak sağlayacak şekilde düzenlenmiştir. Bu yönüyle çalışma, alüminyum iletken üretiminde gerçek zamanlı yapay zeka uygulamaları için gerekli olan görsel veri altyapısını sağlamaktadır. Prototipin tasarımı, düşük maliyetli ve kolay temin edilebilir donanımlar kullanılarak gerçekleştirilmiş olup, üretim hatlarına entegre edilebilir yapıda modüler bir sistem sunmaktadır. Bu çalışmanın temel katkısı, gerçek üretim ortamlarına uygun koşullar altında çalışan bir prototip sistemin geliştirilmesidir. Bu sistem, tutarlı ve ölçeklenebilir veri toplamayı mümkün kılan otomatik görüntü toplama yeteneğine sahiptir. Ayrıca, bu çalışma alüminyum iletkenlerdeki yüzey deformasyonlarını tespit etmek için yeni bir veri kümesi sunar. Geliştirilen altyapı, endüstriyel kalite kontrol sistemleri için yapay zeka tabanlı çözümlerin temelini oluşturmayı hedeflemektedir.

Keywords

• Derin Öğrenme
• Konveyör Sistem
• Hata Tespiti
• Kalite Kontrol
• Yapay Zekâ
• Makine Tasarımı

Design and Production of a Prototype Conveyor Belt for Real-Time Surface Imaging and Data Set Creation

Abstract

In this study, a conveyor belt-based prototype system was developed to create a data set by imaging surface deformations that may occur during the production process of solid aluminum conductors. The system operates using a high-resolution camera integrated into a vision inspection box with controlled lighting and an adjustable viewing angle, capable of capturing images at a resolution of 1280×1024 pixels at a rate of 60 frames per second. The images were collected on the conveyor belt moving at a constant speed, thereby ensuring continuity in image quality. With the developed prototype, a total of 1.600 images were collected and manually labeled as examples of three different defect types. The obtained dataset was organized in a way that will enable the detection and classification of surface defects such as pitting, embossing, and scalping using deep learning algorithms in subsequent stages. In this respect, the study provides the visual data infrastructure necessary for real-time artificial intelligence applications in aluminum conductor production. The prototype was designed using low-cost and easily obtainable hardware and offers a modular system that can be integrated into production lines. The main contribution of this study includes the development of a prototype system that operates under conditions suitable for real production environments. This system is equipped with automated image acquisition capabilities, ensuring consistent and scalable data collection. Additionally, this study provides a new dataset for detecting surface deformations in aluminum conductors. The developed infrastructure aims to form the basis for artificial intelligence-based solutions for industrial quality control systems.

Keywords

• Deep Learning
• Conveyor System
• Defect Detection
• Quality Control
• Artificial Intelligence
• Machine Design

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