Kritik Üretim Sistemlerinde Kenar Bilişimin Önemi: FPGA Uygulaması

Yıl 2022, Sayı: 43, 41 - 47, 30.11.2022

Tuncay Ercan

https://doi.org/10.31590/ejosat.1201855

Öz

Endüstri 4.0 ile birlikte ivme kazanmakla birlikte hayatımızın her alanına girmiş olan akıllı sistemler ile ilgili akademik ve endüstriyel çalışmalar hızla devam etmektedir. Özellikle üretimde kullanılan kalıp, baskı, şekillendirme, kesme vb. gibi farklı makineler, belli bir otomasyona sahiptirler. Bunların işledikleri malzeme bilgileri ve ortak çalışan diğer makinelerle uyumu bütün üretim sisteminin etkin şekilde çalışabilmesi çok önemlidir. Elde edilen verilerin bolluğu daha iyi analitik inceleme için önemliyken, bu verilerden iş amaçlı bilgi elde etmek ve karar destek sistemlerine yardımcı olmak organizasyondaki Bilgi Teknolojileri ve Sistemlerinden beklenen en önemli görevdir. Bu çalışmada üretim ortamındaki cihazlardan, sensörlerden ve işletmedeki diğer Bilgi Sistemlerinden gelen verilerin FPGA tabanlı bir kenar bilişim altyapısı ile değerlendirilmesi önerilmektedir. Bu çalışma kapsamında üretim ortamında kullanılan örnek bir plastik enjeksiyon kalıp cihazına ait bakım kararının tahminlemesi yapılmıştır. Bunun için Internet üzerinden erişilen temsili hız, titreşim ve sıcaklık gibi faktörleri içeren bir veri seti kullanılmış, sensör verileri ve mevcut bilgi sistemi girişleri (ERP, MES) ile gerekli ML algoritmalarını çalıştıracak bir FPGA (Alan Programlanabilir Ağ Dizisi) tasarımı da, Xilinx Design Tools ve Vitis IDE 2020.2 kullanılarak gerçekleştirilmiştir.Veri girişi için üzerinde akıllı algoritmaların çalıştırılacağı FPGA donanımı “Xilinx Zynq xc7z020” kenar bilişim altyapısının esas öğesi olarak planlanmıştır. Çalışmada hibrit bir yaklaşım olan ANFIS (Adaptif Ağ Tabanlı Bulanık Çıkarım Sistemi) sistemi Yapay Zekâ uygulaması olarak seçilmiştir. Elde edilen tahmin sonuçları literatürdeki çalışmalarda erişilen doğruluk oranları üzerinden değerlendirilmiştir.

Anahtar Kelimeler

Kenar Bilişim, Endüstri 4.0, Yeniden Yapılandırma, Akıllı Üretim, FPGA, Bulanık Mantık, ANFIS

Importance of Edge Computing in Critical Manufacturing Systems: FPGA Implementation

Öz

Academic and industrial studies on smart systems, which have entered all areas of our lives, continue to their rapid developments along with gaining momentum with Industry 4.0. Especially, many of the different production devices with their molding, printing, shaping, and cutting capabilities have a certain level of automation. They work together with the material information they process, and the compatibility with other machines to make the whole production system work effectively and properly. While the abundance of data acquired is an important source for better analytics, obtaining information for business purposes from this data and helping decision support systems is the most important task expected from Information Technology and Systems in the organization. In this paper, we propose an FPGA-based edge information infrastructure to evaluate critical data from the production devices, distributed sensors, and other ISs in any industrial environment to increase the utilization and performance of the total machinery. This study helps the predictive maintenance decision for a sample plastic injection molding device according to our industrial scenario. A sample data set downloaded from the Internet with the factors like speed, vibration, and the temperature was used. An FPGA (Field Programmable Gate Array) design that will run the necessary ML algorithms with the sensor data and existing information system inputs (ERP, MES) has been carried out by using Xilinx Design Tools and Vitis IDE 2020.2. In this study, the ANFIS (Adaptive Network-Based Fuzzy Inference System) system, which is an approach consisting of the integration of artificial neural networks and Fuzzy Logic, has been chosen as an Artificial Intelligence application. The estimation results obtained were evaluated over the accuracy rates achieved in similar studies in the literature.

Anahtar Kelimeler

Edge Computing, Industry 4.0, Reconfiguration, Smart Manufacturing, FPGA, Fuzzy Logic, ANFIS

Kaynakça

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