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

Yıl 2021, Cilt: 11 Sayı: 2, 537 - 546, 15.04.2021
https://doi.org/10.17714/gumusfenbil.826323

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.

Kaynakça

  • Gonzalez, R. C. and Woods, R. E. (2008). Digital Image Processing (4th ed.). United States of America: Pearson Prentice Hall
  • Hengdi, W., Yang, Z., Sier, D., Erdong, S. and Yong, W. (2011). Bearing characters recognition system based on LabVIEW. International Conference on Consumer Electronics, Communications, and Networks (CECNet) (pp. 118-122). XianNing. https://doi.org/10.3390/machines9020040
  • Hryniewicz, P., Banaś, W., Gwiazda, A., Foit, K., Sękala, A. and Kost, G. (2015). Technological process supervising using vision systems cooperating with the LabVIEW vision builder. Modern Technologies in Industrial Engineering (pp. 1-6). Mamaia.
  • Jing, N., Guo, N. and Xiong, W. (2016). An efficient tile-pyramids building method for fast visualization of massive geospatial raster datasets. Advances in Electrical and Computer Engineering, 16(4). 3-8.
  • Kalina, D. and Golovanov, R. (2019). Application of template matching for optical character recognition. IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) (pp. 2213-2217). Moscow. https://doi.org/10.1109/EIConRus.2019.8657297
  • Kamtongdee, C., Sumriddetchkajorn, S. and Sa-ngiamsak, C. (2013). Feasibility study of silkworm pupa sex identification with pattern matching. Computers and Electronics in Agriculture, 95, 31-37. https://doi.org/10.1016/j.compag.2013.04.002.
  • Koniar, D., Hargas, L., Simonova, A., Hrianka, M. and Loncova, Z. (2014). Virtual instrumentation for visual inspection in mechatronic applications. Procedia Engineering, 96, 227-234. https://doi.org/10.1016/j.proeng.2014.12.148.
  • National Instruments. (2005). NI Vision Concepts Manual, National Instruments Corporate Headquarters (2000 ed.). USA: National Instruments Corporation.
  • National Instruments. (2018). NI Vision Builder for Automated Inspection Tutorial, National Instruments Corporate Headquarters (2018 ed.). USA; National Instruments Corporation.
  • Panoiu, M., Rat, C. L. and Panoiu, C. (2015). Study on road sign recognition in LabVIEW. IOP Conference Series: Materials Science and Engineering (pp. 1-10). Wuhan.
  • Parmar, R., Shah, M. and Shah, M. G. (2017). A Comparative study on Different ANN Techniques in Wind Speed Forecasting for Generation of Electricity. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), 12 (1). 19-26.
  • Patil, V. and Ingle, D. R. (2020). An association between fingerprint patterns with blood group and lifestyle-based diseases: a review. Artificial Intelligence Review, 1-37. https://doi.org/10.1007/s10462-020-09891-w
  • Plötzeneder, B. (2010). Praxiseinstieg LabVIEW (1st ed.). Germany: Franzis Verlag GmbH.
  • Rouget, P., Badrignans, B., Benoit, P. and Torres, L. (2018). FPGA implementation of pattern matching for industrial control systems. IEEE International Parallel, and Distributed Processing Symposium Workshops (pp. 210-213). Canada.
  • Zhou, B., Hu, W., Brown, K. and Chen, T., D. R. (2020). Generalized pattern matching of industrial alarm flood sequences via word processing and sequence alignment. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/TIE.2020.3026287

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, 537 - 546, 15.04.2021
https://doi.org/10.17714/gumusfenbil.826323

Öz

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.

Kaynakça

  • Gonzalez, R. C. and Woods, R. E. (2008). Digital Image Processing (4th ed.). United States of America: Pearson Prentice Hall
  • Hengdi, W., Yang, Z., Sier, D., Erdong, S. and Yong, W. (2011). Bearing characters recognition system based on LabVIEW. International Conference on Consumer Electronics, Communications, and Networks (CECNet) (pp. 118-122). XianNing. https://doi.org/10.3390/machines9020040
  • Hryniewicz, P., Banaś, W., Gwiazda, A., Foit, K., Sękala, A. and Kost, G. (2015). Technological process supervising using vision systems cooperating with the LabVIEW vision builder. Modern Technologies in Industrial Engineering (pp. 1-6). Mamaia.
  • Jing, N., Guo, N. and Xiong, W. (2016). An efficient tile-pyramids building method for fast visualization of massive geospatial raster datasets. Advances in Electrical and Computer Engineering, 16(4). 3-8.
  • Kalina, D. and Golovanov, R. (2019). Application of template matching for optical character recognition. IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) (pp. 2213-2217). Moscow. https://doi.org/10.1109/EIConRus.2019.8657297
  • Kamtongdee, C., Sumriddetchkajorn, S. and Sa-ngiamsak, C. (2013). Feasibility study of silkworm pupa sex identification with pattern matching. Computers and Electronics in Agriculture, 95, 31-37. https://doi.org/10.1016/j.compag.2013.04.002.
  • Koniar, D., Hargas, L., Simonova, A., Hrianka, M. and Loncova, Z. (2014). Virtual instrumentation for visual inspection in mechatronic applications. Procedia Engineering, 96, 227-234. https://doi.org/10.1016/j.proeng.2014.12.148.
  • National Instruments. (2005). NI Vision Concepts Manual, National Instruments Corporate Headquarters (2000 ed.). USA: National Instruments Corporation.
  • National Instruments. (2018). NI Vision Builder for Automated Inspection Tutorial, National Instruments Corporate Headquarters (2018 ed.). USA; National Instruments Corporation.
  • Panoiu, M., Rat, C. L. and Panoiu, C. (2015). Study on road sign recognition in LabVIEW. IOP Conference Series: Materials Science and Engineering (pp. 1-10). Wuhan.
  • Parmar, R., Shah, M. and Shah, M. G. (2017). A Comparative study on Different ANN Techniques in Wind Speed Forecasting for Generation of Electricity. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), 12 (1). 19-26.
  • Patil, V. and Ingle, D. R. (2020). An association between fingerprint patterns with blood group and lifestyle-based diseases: a review. Artificial Intelligence Review, 1-37. https://doi.org/10.1007/s10462-020-09891-w
  • Plötzeneder, B. (2010). Praxiseinstieg LabVIEW (1st ed.). Germany: Franzis Verlag GmbH.
  • Rouget, P., Badrignans, B., Benoit, P. and Torres, L. (2018). FPGA implementation of pattern matching for industrial control systems. IEEE International Parallel, and Distributed Processing Symposium Workshops (pp. 210-213). Canada.
  • Zhou, B., Hu, W., Brown, K. and Chen, T., D. R. (2020). Generalized pattern matching of industrial alarm flood sequences via word processing and sequence alignment. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/TIE.2020.3026287
Toplam 15 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Burak Güzelce 0000-0002-9353-1016

Gökay Bayrak 0000-0002-5136-0829

Yayımlanma Tarihi 15 Nisan 2021
Gönderilme Tarihi 17 Kasım 2020
Kabul Tarihi 15 Mart 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 11 Sayı: 2

Kaynak Göster

APA Güzelce, B., & Bayrak, G. (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. https://doi.org/10.17714/gumusfenbil.826323