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Derin Öğrenme Tabanlı Kalite Kontrol Uygulaması

Year 2020, Ejosat Special Issue 2020 (ICCEES), 332 - 336, 05.10.2020
https://doi.org/10.31590/ejosat.804744

Abstract

Bu çalışma, derin öğrenme tabanlı bir kalite kontrol uygulaması ile ilgilidir. Kalite kontrol üretim aşamasının önemli bir safhasıdır. Bu süreç sayesinde üretimde oluşmuş olan ürün üzerindeki hataların tanımlanması ve tüketiciye yansıtılmaması hedeflenir. Günümüzde üretim tesislerinde ürün kontrolü için genellikle uzman kişiler çalıştırılmaktadır. Uzman kişiler tarafından sorunsuz ve sorunlu ürün arasındaki farklar kolaylıkla anlaşılabilmektedir. Öte yandan üretim hattının büyümesi, insan kaynaklı genel problemler bu türde uygulamalar için bir sorun teşkil etmektedir. Bu kapsamda uzman kişilerin yerini alabilecek bilgisayar destekli algoritmalara sıklıkla ihtiyaç duyulmaktadır. Bilgisayar tabanlı yazılımlar sayesinde ürün kontrol süreci hızlandırılabilmektedir. Ayrıca insan kaynaklı olası problemlerin önüne geçmekte mümkün olmaktadır. Bu yazılımlar koşullandırılmış çalışma şartlarında yüksek etkinlik ve doğrulukla çalışabilmesine karşın bir insanın kolaylıkla çözebileceği basit hataların üstesinden gelememektedir. Bu yüzden çoğu zaman olumlu taraflarına rağmen tercih edilmemektedir. Bu noktada son yıllarda ön plana çıkan derin öğrenme tabanlı yapay zeka algoritmaları sayesinde önemli bir gelişme sağlanmıştır. GPU’ların gelişmesi ve fiyatlarının erişilebilir olması sebebiyle çok fazla örnekle eğitim yapılabilmesinin önü açılmıştır. Örnek sayısının artması eğitim sürecinde çok daha iyi bir ağın oluşmasına imkan sağlamakta, artan hız gereksinimi de GPU’lar sayesinde karşılanabilmektedir. Çalışma içerisinde bahsedilen şemayı sağlayabilecek bir uygulama üzerinde durulmuştur. Bir inverterin üretim hattında robotlar tarafından bağlanan frenleme direnci kablolarının kontrolü için derin öğrenmenin bir alt kolu olan CNN tabanlı algoritmalar kullanılmıştır. Böylece bir bant üzerinden akan ürünlerin kablolarının bağlanması veya unutulması durumuna göre hatalı / sorunsuz ürünler tespit edilebilmiştir.

References

  • Basile, F., Chiacchio, P., & Gerbasio, D. (2012). On the implementation of industrial automation systems based on PLC. IEEE Transactions on Automation Science and Engineering, 10(4), 990-1003.
  • Butuza, R., Nascu, I., Giurgioiu, O., & Crisan, R. (2014). Automation system based on SIMATIC S7 300 PLC, for a hydro power plant. Paper presented at the 2014 IEEE International Conference on Automation, Quality and Testing, Robotics.
  • Çimen, H., Palacios-Garcia, E. J., Çetinkaya, N., Kolbæk, M., Sciumè, G., Vasquez, J. C., & Guerrero, J. M. (2020). Generalization Capacity Analysis of Non-Intrusive Load Monitoring using Deep Learning. Paper presented at the 2020 IEEE 20th Mediterranean Electrotechnical Conference (MELECON).
  • Lehr, J., Schlüter, M., & Krüger, J. (2019). Classification of Similar Objects of Different Sizes Using a Reference Object by Means of Convolutional Neural Networks. Paper presented at the 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA).
  • Li, L., Yu, Y., Bai, S., Hou, Y., & Chen, X. (2017). An Effective Two-Step Intrusion Detection Approach Based on Binary Classification and $ k $-NN. IEEE Access, 6, 12060-12073.
  • Mazur, D. C., Quint, R. D., & Centeno, V. A. (2012). Time synchronization of automation controllers for power applications. Paper presented at the 2012 IEEE Industry Applications Society Annual Meeting.
  • Ruan, X., Ren, D., Zhu, X., & Huang, J. (2019). Mobile robot navigation based on deep reinforcement learning. Paper presented at the 2019 Chinese control and decision conference (CCDC).
  • Truby, R. L., Della Santina, C., & Rus, D. (2020). Distributed Proprioception of 3D Configuration in Soft, Sensorized Robots via Deep Learning. IEEE Robotics and Automation Letters, 5(2), 3299-3306.
  • Väänänen, M., Horelli, J., & Katajisto, J. (2010). Virtual learning environment concept for PLC-programming-case: Building automation. Paper presented at the 2010 2nd International Conference on Education Technology and Computer.
  • Wu, J., Huang, L., & Pan, X. (2010). A novel bayesian additive regression trees ensemble model based on linear regression and nonlinear regression for torrential rain forecasting. Paper presented at the 2010 Third International Joint Conference on Computational Science and Optimization.

A Deep Learning-Based Quality Control Application

Year 2020, Ejosat Special Issue 2020 (ICCEES), 332 - 336, 05.10.2020
https://doi.org/10.31590/ejosat.804744

Abstract

The study at hand is an implementation of a deep learning strategy on a quality control scheme. The quality control process is a substantial part of product manufacturing. It fundamentally targets to detect and eliminate defective products so that the erroneous ones will not be delivered to the customers. Final product control has been usually performed by experts. Generally, those experts can easily distinguish defective and trouble-free products. On the other hand, growing product lines and human-based natural problems may affect the efficiency of that quality control process. Therefore, there is an increasing demand for computer-aided software that will take the place of those experts. This software or algorithm typically increases the product control rate. Besides, they make it possible to avoid from human-driven faults. The algorithms run at high speed and efficacy under conditional situations i.e. perfectly lightening environment. However, they may easily fail when small changes occur in the environment or the product for some duties that humans can easily achieve. These robustness problems make them not preferable, although they have numerous advantages. At this point, deep learning-based artificial intelligence algorithms have made a significant enhancement. The general development and achievable prices of GPUs pave the way for using numerous training examples so that better networks, meaning more robust, can be created for the applications. To this end, we carried on an experiment that could realize the deep learning strategy on the quality control scheme. For this purpose, the developed algorithms applied to the inverters conveying on a product line to confirm whether they are erroneous or not. Results show that developed strategy could detect defective products similar to the human being.

References

  • Basile, F., Chiacchio, P., & Gerbasio, D. (2012). On the implementation of industrial automation systems based on PLC. IEEE Transactions on Automation Science and Engineering, 10(4), 990-1003.
  • Butuza, R., Nascu, I., Giurgioiu, O., & Crisan, R. (2014). Automation system based on SIMATIC S7 300 PLC, for a hydro power plant. Paper presented at the 2014 IEEE International Conference on Automation, Quality and Testing, Robotics.
  • Çimen, H., Palacios-Garcia, E. J., Çetinkaya, N., Kolbæk, M., Sciumè, G., Vasquez, J. C., & Guerrero, J. M. (2020). Generalization Capacity Analysis of Non-Intrusive Load Monitoring using Deep Learning. Paper presented at the 2020 IEEE 20th Mediterranean Electrotechnical Conference (MELECON).
  • Lehr, J., Schlüter, M., & Krüger, J. (2019). Classification of Similar Objects of Different Sizes Using a Reference Object by Means of Convolutional Neural Networks. Paper presented at the 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA).
  • Li, L., Yu, Y., Bai, S., Hou, Y., & Chen, X. (2017). An Effective Two-Step Intrusion Detection Approach Based on Binary Classification and $ k $-NN. IEEE Access, 6, 12060-12073.
  • Mazur, D. C., Quint, R. D., & Centeno, V. A. (2012). Time synchronization of automation controllers for power applications. Paper presented at the 2012 IEEE Industry Applications Society Annual Meeting.
  • Ruan, X., Ren, D., Zhu, X., & Huang, J. (2019). Mobile robot navigation based on deep reinforcement learning. Paper presented at the 2019 Chinese control and decision conference (CCDC).
  • Truby, R. L., Della Santina, C., & Rus, D. (2020). Distributed Proprioception of 3D Configuration in Soft, Sensorized Robots via Deep Learning. IEEE Robotics and Automation Letters, 5(2), 3299-3306.
  • Väänänen, M., Horelli, J., & Katajisto, J. (2010). Virtual learning environment concept for PLC-programming-case: Building automation. Paper presented at the 2010 2nd International Conference on Education Technology and Computer.
  • Wu, J., Huang, L., & Pan, X. (2010). A novel bayesian additive regression trees ensemble model based on linear regression and nonlinear regression for torrential rain forecasting. Paper presented at the 2010 Third International Joint Conference on Computational Science and Optimization.
There are 10 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Mehmet Korkmaz 0000-0002-1462-8005

Mücahid Barstuğan 0000-0001-9790-5890

Publication Date October 5, 2020
Published in Issue Year 2020 Ejosat Special Issue 2020 (ICCEES)

Cite

APA Korkmaz, M., & Barstuğan, M. (2020). A Deep Learning-Based Quality Control Application. Avrupa Bilim Ve Teknoloji Dergisi332-336. https://doi.org/10.31590/ejosat.804744