Endüstriyel Süreçlerin Dijitalleştirilmesi için Yol Haritası

Yıl 2020, Ejosat Özel Sayı 2020 (ARACONF), 349 - 357, 01.04.2020

Yeliz İleri Murat Furat

https://doi.org/10.31590/ejosat.araconf45

Öz

Sanayiyi ve toplumu dijital alana taşımak son yıllarda giderek artmıştır. Dijitalleşme, yaşamımızı daha verimli ve hızlı hale getirmiştir. Dijitalleşmenin bir sonucu olarak, Endüstri 4.0 olarak da bilinen dördüncü sanayi devrimi doğdu. Dördüncü Sanayi Devrimi de önceki devrimler gibi, yaşamı kolaylaştırmayı ve yaşam kalitesini arttırmayı amaçlar. Dijitalleşme için iki terim özellikle önemlidir. Bu terimler Endüstri 4.0 ve Nesnelerin İnterneti (IoT) 'dir. Endüstri 4.0 ve IoT, hem insan hem de endüstri kullanılan cihazların ağ ve otomasyonuna odaklanmaktadır. Dijitalleşme ve IoT teknolojileri, enerji verimliliğini artırmada önemli bir rol oynamaktadır. Dijital ikiz (DT) teknolojisi, dijitalleşme altındaki Endüstri 4.0 teknolojilerinin en önemli anahtarlarından biridir. DT, fiziksel bir sistemin veya varlığın dijital bir kopyasını temsil eder. Oluşturulan fiziksel nesneler ve DT modelleri birbirleriyle etkileşime girer. DT teknolojileri imalattan sağlık sektörlerine ve deniz taşımacılığından ağır sanayiye kadar geniş bir yelpazeye sahiptir. DT, endüstrilerin dijitalleşme ile büyümesi için birçok kolaylık sağlıyor. Öte yandan DT, akademik alandaki gelişimi ile de dikkat çekmektedir. Bu çalışmada DT'ye genel bir bakış sunulmuş, bir DT yol haritası önerilmiş, endüstri süreçleri için DT kavramında ortaya çıkan bazı algoritmalar açıklanmış ve örnek bir olay analiz edilmiştir. Bu yol haritası, gelecekteki uygulamaları ve DT ile sürdürülebilir üretim için doğru tahmin yapmak amacıyla uygulanacak farklı yöntemleri göstermektedir. Sonuç olarak, DT kavramının yakın gelecekte çok fazla dikkat çekeceği düşünülmektedir.

Anahtar Kelimeler

Dijitalleşme, Endüstri, Dijital İkiz, Enerji Verimliliği, Tahmin Algoritmaları

A Roadmap for Digitalization of Industrial Processes

Öz

Migrating the industry and society into digital area have gradually been increased in recent years. Digitalization has made our life more efficient and faster. As a result of digitalization, the Fourth Industrial Revolution, also known as Industry 4.0, was born. The Fourth Industrial Revolution, like previous revolutions, aims to facilitate life and improve the quality of life. Two terms are especially important for digitalization. These terms are Industry 4.0 and Internet of Things (IoT). Industry 4.0 and the IoT focus on the networking and automation of devices both used human being and industry. Digitalization and IoT technologies play an important role in improving energy efficiency. Digital twin (DT) technology is one of the most important key of Industry 4.0 technologies under digitalization. DT represents a digital copy of a physical system or entity. Physical objects and DT models created interact with each other. DT technologies have a wide range from manufacturing to healthcare sectors and from maritime transportation to the heavy industry. DT provides many conveniences for industries to grow with digitalization. Meanwhile, DT draws attention with its development in the academic field. In this study, an overview of DT is presented, a DT roadmap is proposed, some algorithms emerging in the concept of DT for industrial processes are explained and a case study is analyzed. This roadmap shows different methods to be applied in order to make accurate forecasting for the future applications and sustainable manufacturing with DT. As a result, DT concept is thought to attract much attention in the near future.

Anahtar Kelimeler

Digitalization, Industry, Digital Twin, Energy Efficiency, Forecast Algorithms

Kaynakça

• Maslarić, M., Nikoličić, S., & Mirčetić, D. (2016). Logistics response to the industry 4.0: the physical internet. Open engineering, 6(1).
• Finance, A. T. C. C. (2015). Industry 4.0 Challenges and solutions for the digital transformation and use of exponential technologies. Finance, Audit Tax Consulting Corporate: Zurich, Swiss.
• Schleich, B., Anwer, N., Mathieu, L., & Wartzack, S. (2017). Shaping the digital twin for design and production engineering. CIRP Annals, 66(1), 141-144.
• Grieves, M. (2018). Digital Twin: Manufacturing Excellence through Virtual Factory Replication, Available online: https://www.researchgate.net/publication/275211047_Digital_Twin_Manufacturing_Excellence_through_Virtual_Factory_Replication
• Glaessgen, E., & Stargel, D. (2012, April). The digital twin paradigm for future NASA and US Air Force vehicles. In 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference 20th AIAA/ASME/AHS adaptive structures conference 14th AIAA (p. 1818).
• Marr, B. What is digital twin technology-and why is it so important? Forbes, 6 March 2017.
• Pettey, C. (2017). Prepare for the impact of digital twins. Gartner: Stamford, CT, USA.
• Reid, J. B., & Rhodes, D. H. (2016, March). Digital System Models: An investigation of the non-technical challenges and research needs. In Conference on Systems Engineering Research.
• Madni, A. M., Madni, C. C., & Lucero, S. D. (2019). Leveraging digital twin technology in model-based systems engineering. Systems, 7(1), 7.
• Makarov, V. V., Frolov, Y. B., Parshina, I. S., & Ushakova, M. V. (2019, October). The Design Concept of Digital Twin. In 2019 Twelfth International Conference" Management of large-scale system development"(MLSD) (pp. 1-4). IEEE.
• Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., ... & Nee, A. Y. C. (2019). Enabling technologies and tools for digital twin. Journal of Manufacturing Systems, https://doi.org/10.1016/j.jmsy.2019.10.001.
• Tao, F., Zhang, M., Liu, Y., & Nee, A. Y. C. (2018). Digital twin driven prognostics and health management for complex equipment. CIRP Annals, 67(1), 169-172.
• Qiao, Q., Wang, J., Ye, L., & Gao, R. X. (2019). Digital Twin for Machining Tool Condition Prediction. Procedia CIRP, 81, 1388-1393.
• Brownlee, J. (2017). How to create an ARIMA model for time series forecasting with Python. Machine Learning Mastery. Saatavissa: https://machinelearningmastery. com/arima-for-time-series-forecasting-with-python/. Hakupäivä, 2, 2019.
• Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.(Chapter 8), Avalilable online: https://otexts.com/fpp2/arima.html
• Time series Forecasting — ARIMA models, https://towardsdatascience.com/time-series-forecasting-arima-models-7f221e9eee06, last access: 15 February 2020.
• Wang, J., Yan, J., Li, C., Gao, R. X., & Zhao, R. (2019). Deep heterogeneous GRU model for predictive analytics in smart manufacturing: Application to tool wear prediction. Computers in Industry, 111, 1-14.
• Wang, J., Yang, W., Du, P., & Niu, T. (2018). A novel hybrid forecasting system of wind speed based on a newly developed multi-objective sine cosine algorithm. Energy Conversion and Management, 163, 134-150.
• Islam, M. M. (2019). Ship Smart System Design (S3D) and Digital Twin, Book Chapter in VFD Challenges for Shipboard Electrical Power System Design, Wiley-IEEE Press.
• Üzümcü, S., Mert, A. A., & Atay, F. (2019, July). Usage of Digital Twin Technologies during System Modeling and Testing in Vessel Traffic Services System Project. In INCOSE International Symposium (Vol. 29, No. 1, pp. 189-202).
• Flora, M., Fröch, G., & Gächter, W. (2020). Optimierung des Baumanagements im Untertagebau mittels digitaler Infrastruktur‐Informationsmodelle. Bautechnik.
• Curl, J. M., Nading, T., Hegger, K., Barhoumi, A., & Smoczynski, M. (2019). Digital Twins: The Next Generation of Water Treatment Technology. Journal‐American Water Works Association, 111(12), 44-50.
• Hyeong-su, K., Jin-Woo, K., Yun, S., & Kim, W. T. (2019, July). A novel wildfire digital-twin framework using interactive wildfire spread simulator. In 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 636-638). IEEE.
• Peng, Y., & Wang, H. (2019). Application of Digital Twin Concept in Condition Monitoring for DC-DC Converter. In 2019 IEEE Energy Conversion Congress and Exposition (ECCE) (pp. 2199-2204). IEEE.
• Karakra, A., Fontanili, F., Lamine, E., & Lamothe, J. (2019, May). HospiT'Win: A Predictive Simulation-Based Digital Twin for Patients Pathways in Hospital. In 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) (pp. 1-4). IEEE.
• Bachelor, G., Brusa, E., Ferretto, D., & Mitschke, A. (2019). Model-Based Design of Complex Aeronautical Systems Through Digital Twin and Thread Concepts. IEEE Systems Journal.
• Luo, W., Hu, T., Zhu, W., & Tao, F. (2018, March). Digital twin modeling method for CNC machine tool. In 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC) (pp. 1-4). IEEE.
• Vachálek, J., Bartalský, L., Rovný, O., Šišmišová, D., Morháč, M., & Lokšík, M. (2017, June). The digital twin of an industrial production line within the industry 4.0 concept. In 2017 21st International Conference on Process Control (PC) (pp. 258-262). IEEE.