Journals / İmalat Teknolojileri ve Uygulamaları / 2026 / Cilt: 5 Sayı: 2

Detecting Manufacturing Defects on Printed Circuit Boards Using Metamaterial-Based Circular Microstrip Patch Antenna

Sultan Suheyla Bakir(ANKARA YILDIRIM BEYAZIT UNIVERSITY)0009-0009-6963-0610
Asaf Behzat Sahin(ANKARA YILDIRIM BEYAZIT ÜNİVERSİTESİ)0000-0001-9759-8448
Pages
65–74
Publication Date
2024-08-30
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Since the samples to be measured are not irreversibly damaged during the testing process, the proposed design can be considered a non-destructive measurement method to provide information about the type and location of defects with real-time measurement data.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, a microstrip circular patch antenna is designed having a metamaterial-based (MTM) ground plane to detect manufacturing defects of short circuits and open circuits on printed circuit boards (PCB).
📊
Key Findings & Data:In that regard, PCB specimens of FR-4 as the substrate and copper microstrip lines as the conductive wiring lines are designed and manufactured.
💡
Conclusion & Impact:Since the samples to be measured are not irreversibly damaged during the testing process, the proposed design can be considered a non-destructive measurement method to provide information about the type and location of defects with real-time measurement data.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Baskılı Devre Kartı · Metamalzemeler · Dairesel Yama Anten

Abstract

In this study, a microstrip circular patch antenna is designed having a metamaterial-based (MTM) ground plane to detect manufacturing defects of short circuits and open circuits on printed circuit boards (PCB). In that regard, PCB specimens of FR-4 as the substrate and copper microstrip lines as the conductive wiring lines are designed and manufactured. Five vertical copper lines printed side by side on the top of the substrate are used to demonstrate the working mechanism of the designed antenna sensor. Two different defect scenarios of open and short circuits with controlled locations are studied to determine variations in the return loss data of the proposed structure. MTM cell structures with cross lines enclosed by an octagon ring are periodically placed as part of the ground plane of the proposed antenna to obtain higher sensitivity of the designed and manufactured sensor. Antenna return loss behaviors in terms of the locations of the faults are employed to prepare a database to detect not only the presence of the faults but also determine their locations. Since the samples to be measured are not irreversibly damaged during the testing process, the proposed design can be considered a non-destructive measurement method to provide information about the type and location of defects with real-time measurement data.

Özet

Bu çalışmada, baskılı devre kartları (PCB) üzerindeki kısa devre ve açık devrelerden kaynaklanan imalat hatalarını tespit etmek için metamalzeme tabanlı (MTM) yer düzlemine sahip bir mikroşerit dairesel yama anteni tasarlanmıştır. Bu bağlamda altlık olarak FR-4 PCB numuneleri ve iletken kablolama hatları olarak bakır mikroşerit hatları tasarlanıp üretilmektedir. Alt tabakanın üst kısmında yan yana basılmış beş dikey bakır çizgi, tasarlanan anten sensörünün çalışma mekanizmasını göstermek için kullanılmıştır. Önerilen yapının geri dönüş kaybı verilerindeki değişiklikleri belirlemek için konumları kontrollü açık ve kısa devrelerden oluşan iki farklı arıza senaryosu incelenmiştir. Tasarlanan ve üretilen sensörün daha yüksek hassasiyetini elde etmek için, sekizgen bir halka ile çevrelenmiş çapraz çizgili MTM hücre yapıları, önerilen antenin yer düzleminin bir parçası olarak periyodik olarak yerleştirilmektedir. Arızaların konumlarına göre anten geri dönüş kaybı davranışları, arızaların sadece varlığını tespit etmekle kalmayıp aynı zamanda konumlarını da tespit etmek için bir veri tabanı hazırlamak amacıyla kullanılmıştır. Ölçülecek numuneler test sürecinde geri dönülemez bir hasara uğramadığından, önerilen tasarım, gerçek zamanlı ölçüm verileriyle kusurların türü ve konumu hakkında bilgi sağlayan, tahribatsız bir ölçüm yöntemi olarak değerlendirilebilir.

🔖

Cite This Article

Bakir, S. S. (2024). Detecting Manufacturing Defects on Printed Circuit Boards Using Metamaterial-Based Circular Microstrip Patch Antenna. İmalat Teknolojileri ve Uygulamaları, Cilt: 5 Sayı: 2, 65–74. https://doi.org/10.52795/mateca.1475763

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (12)

  1. W. Jillek, W. Yung, Embedded components in printed circuit boards: a processing technology review. International Journal of Advanced Manufacturing Technologies, 25: 350–360, 2005.
  2. A.C. Marques, J.-M. Cabrera, C.F. Malfatti, Printed circuit boards: A review on the perspective of sustainability, Journal of Environmental Management, 131: 298-306, 2013.
  3. K.P. Anoop, N.S. Sarath, V.V. Sasi Kumar, A review of PCB defect detection using image processing, International Journal of Engineering and Innovative Technology 4(11): 188-192, 2015.
  4. H. Rau, C-H. Wu, Automatic optical inspection for detecting defects on printed circuit board inner layers, The International Journal of Advanced Manufacturing Technology, 25: 940-946, 2005.
  5. W. Dai, A. Mujeeb, M. Erdt, A. Sourin, Soldering defect detection in automatic optical inspection, Advanced Engineering Informatics, 43: 101004, 2020.
  6. N. Asadizanjani, S. Shahbazmohamadi, M. Tehranipoor, D. Forte, Non-destructive pcb reverse engineering using x-ray micro computed tomography, International Symposium for Testing and Failure Analysis (ISTFA), 1-5 November 2015, Oregon, USA.
  7. Y. Zhou, M. Yuan, J. Zhang, G. Ding, S. Qin. Review of vision-based defect detection research and its perspectives for printed circuit board, Journal of Manufacturing Systems, 70: 557-578, 2023.
  8. G. Mahalingam, K. M. Gay, K. Ricanek. Pcb-metal: A pcb image dataset for advanced computer vision machine learning component analysis, 16th International Conference on Machine Vision Applications (MVA), 27-31 May 2019, Tokyo, Japan.
  9. J. Schmitt, J. Bönig, T. Borggräfe, G. Beitinger, J. Deuse. Predictive model-based quality inspection using machine learning and edge cloud computing, Advanced Engineering Informatics, 45: 101101, 2020.
  10. C.A. Balanis, Antenna theory analysis and design, 3rd Ed., John Wiley & Sons, New Jersey, 2005.
  11. V.G. Veselago, The electrodynamics of substances with simultaneously negative values of ε and μ, Soviet Physics Uspekhi, 10, 509-514, 1968.
  12. V. Kaya, İ. Akgül, Detection of defects in printed circuit boards with machine learning and deep learning algorithms, European Journal of Science and Technology, 41: 183-186, 2022.