Üretim Planlama ve Kontrol Süreçlerinde Dijital İkiz Teknolojisinin Kullanılması: Tekstil Sektöründe Bir Uygulama

Year 2022, Volume: 10 Issue: 4, 711 - 732, 30.12.2022

Aysel Koçak Aytaç Yıldız

https://doi.org/10.29109/gujsc.1170021

Abstract

Bu çalışmada, hazır giyim sektöründe faaliyet gösteren öncü bir firmanın üretim tesisinde, veri dijitalleştirme projesi kapsamında süreçlerin uçtan uca incelenmesi ve yeni nesil bilgi teknolojileri kullanılarak veri odaklı süreç tasarımlarının yapılması amaçlanmıştır. Buna bağlı olarak, yeni gelişen ve hızlı büyüyen bir teknoloji olan dijital ikiz modellerinin yapılabilmesi için gerekli olan süreç altyapılarının oluşturulması hedeflenmiştir. Yapılan çalışmada ilk olarak, süreç haritaları oluşturulmuş ve süreçlere ait sürekli değişen verilerin sensörler ve arayüzler yardımıyla elde edilerek sisteme aktarılması sağlanmıştır. Daha sonra, üretim hattındaki makinelerden alınan süreç bazlı süreler ile üretilecek ürüne ait nitelikler arasında bağlantı kurularak, herhangi bir ürünün sürece girdiğinde ne kadar sürede tamamlanacağı lineer regresyon, polinomal regresyon, gradyan destekli karar ormanı regresyonu ve rassal orman regresyon algoritmaları kullanılarak Knime platformunda tahmin edilmiştir. Yapılan tahmin sonuçlarına göre rassal orman regresyon modelinin, en yüksek R2 ve en düşük hata metrik değerlerine sahip olduğu tespit edilmiş ve bu regresyon modeli ERP altyapısına entegre edilmiştir. Ayrıca, tahmin edilen üretim süreleri ve hat üzerindeki çeşitli parametrelere göre üretim çizelgeleme çalışması tasarımı yapılmıştır. Yapılan çalışma, kendi kendine karar verebilen akıllı bir sistemin altyapısının oluşturulması bakımından önemli olup süreçlerin dijital ikizlerinin oluşturulmasında katkı sağlayacağı öngörülmektedir.

Keywords

endüstri 4,0

References

• 1. Karagöz, A., Yıldız, A., Dijital ikiz teknolojisinin üretim ve tasarım sistemlerinde kullanılması, 5. Uluslararası Mühendislik Mimarlık ve Tasarım Kongresi, 21-22 Aralık, İstanbul, Türkiye, 2019.
• 2. Carolis, A., Macchi, M., Negri, E., Terzi, S., Guiding manufacturing companies towards digitalization a methodology for supporting manufacturing companies in defining their digitalization roadmap, In 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC), IEEE, 487-495, 2017.
• 3. Tao, F., Zhang, M., Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing, IEEE Access, 5, 20418-20427, 2017.
• 4. Cattaneo, L., Macchi, M., A digital twin proof of concept to support machine prognostics with low availability of run-to-failure data, IFAC-PapersOnLine, 52(10), 37-42, 2019.
• 5. Barricelli, B. R., Casiraghi, E., Fogli, D., A survey on digital twin: Definitions, characteristics, applications, and design implications, IEEE access, 7, 167653-167671, 2019.
• 6. Khajavi, S. H., Motlagh, N. H., Jaribion, A., Werner, L. C., Holmström, J., Digital twin: vision, benefits, boundaries, and creation for buildings, IEEE Access, 7, 147406-147419, 2019.
• 7. Kiraz, A., Canpolat, O., Ozkurt, C., Taskin, H., Sarp, E., Examination of the criteria affecting Industry 4.0 with structural equation model and a pilot study, Journal of the Faculty of Engineering and Architecture of Gazi University 35(4), 2183-2196, 2020.
• 8. Bortolini, M., Ferrari, E., Gamberi, M., Pilati, F., Faccio, M., Assembly system design in the industry 4.0 era: a general framework, IFAC-PapersOnLine, 50(1), 5700-5705, 2017.
• 9. Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., ... Nee, A. Y., Digital twin-driven product design framework, International Journal of Production Research, 57(12), 3935-3953, 2019.
• 10. Grieves, M., Digital twin: manufacturing excellence through virtual factory replication, White paper, 1, 1-7, 2014.
• 11. Glaessgen, E., Stargel, D., 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, 1818, 2012.
• 12. Rosen, R., Von Wichert, G., Lo, G., Bettenhausen, K. D., About the importance of autonomy and digital twins for the future of manufacturing, IFAC-PapersOnLine, 48(3), 567-572, 2015.
• 13. Qi, Q., Tao, F., Digital twin and big data towards smart manufacturing and industry 4.0: 360-degree comparison, IEEE Access, 6, 3585-3593, 2018.
• 14. Tao, F., & Qi, Q., Make more digital twins, Nature, 573, 490-491, 2019.
• 15. Wang, K. J., Lee, Y. H., Angelica, S., Digital twin design for real-time monitoring–a case study of die cutting machine, International Journal of Production Research, 59(21), 6471-6485, 2021.
• 16. Abramovici, M., Göbel, J. C., Dang, H. B., Semantic data management for the development and continuous reconfiguration of smart products and systems, CIRP Annals, 65(1), 185-188, 2016.
• 17. Schluse, M., Rossmann, J., From simulation to experimentable digital twins: Simulation-based development and operation of complex technical systems, In 2016 IEEE International Symposium on Systems Engineering (ISSE), IEEE, 1-6, 2016.
• 18. Kraft, E. M., The air force digital thread/digital twin-life cycle integration and use of computational and experimental knowledge, In 54th AIAA aerospace sciences meeting, 0897, 2016.
• 19. Liu, Z., Chen, W., Zhang, C., Yang, C., Cheng, Q., Intelligent scheduling of a feature-process-machine tool supernetwork based on digital twin workshop, Journal of manufacturing systems, 58, 157-167, 2021.
• 20. Schleich, B., Anwer, N., Mathieu, L., Wartzack, S., Shaping the digital twin for design and production engineering, CIRP Annals, 66(1), 141-144, 2017.
• 21. Tuegel, E. J., Ingraffea, A. R., Eason, T. G., Spottswood, S. M., Reengineering aircraft structural life prediction using a digital twin, International Journal of Aerospace Engineering, 154798, 2011.
• 22. Lee, J., Kao, H. A., Yang, S., Service innovation and smart analytics for industry 4.0 and big data environment, Procedia Cirp, 16, 3-8, 2014.
• 23. Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., ... Nee, A. Y. C., Enabling technologies and tools for digital twin, Journal of Manufacturing Systems, 58, 3-21, 2021.
• 24. Zheng, Y., Yang, S., Cheng, H., An application framework of digital twin and its case study, Journal of Ambient Intelligence and Humanized Computing, 10(3), 1141-1153, 2019.
• 25. Gockel, B., Tudor, A., Brandyberry, M., Penmetsa, R., Tuegel, E., Challenges with structural life forecasting using realistic mission profiles, In 53rd AIAA/ASME/ASCE/AHS/ASC structural dynamics and materials conference, 1813, 2012.
• 26. Seshadri, B. R., Krishnamurthy, T., Structural health management of damaged aircraft structures using digital twin concept, In 25th AIAA/AHS Adaptive Structures Conference, 1675, 2017.
• 27. Um, J., Weyer, S., Quint, F., Plug-and-Simulate within modular assembly line enabled by digital twins and the use of automationML, IFAC-PapersOnLine, 50(1), 15904-15909, 2017.
• 28. Zhang, H., Liu, Q., Chen, X., Zhang, D., Leng, J., A digital twin-based approach for designing and multi-objective optimization of hollow glass production line, IEEE Access, 5, 26901-26911, 2017.
• 29. Magargle, R., Johnson, L., Mandloi, P., Davoudabadi, P., Kesarkar, O., Krishnaswamy, S., ... Pitchaikani, A., A simulation-based digital twin for model-driven health monitoring and predictive maintenance of an automotive braking system, In Proceedings of the 12th International Modelica Conference, Prague, Czech Republic, May 15-17, 132, 35-46, 2017.
• 30. Vathoopan, M., Johny, M., Zoitl, A., Knoll, A., Modular fault ascription and corrective maintenance using a digital twin, IFAC-PapersOnLine, 51(11), 1041-1046, 2018.
• 31. Coronado, P. D. U., Lynn, R., Louhichi, W., Parto, M., Wescoat, E., Kurfess, T., Part data integration in the Shop Floor Digital Twin: Mobile and cloud technologies to enable a manufacturing execution system, Journal of manufacturing systems, 48, 25-33, 2018.
• 32. Cunbo, Z., Liu, J., Xiong, H., Digital twin-based smart production management and control framework for the complex product assembly shop-floor, The international journal of advanced manufacturing technology, 96(1-4), 1149-1163, 2018.
• 33. Liau, Y., Lee, H., Ryu, K., Digital Twin concept for smart injection molding, In IOP Conference Series: Materials Science and Engineering, 324(1), IOP Publishing, 012077, 2018.
• 34. Botkina, D., Hedlind, M., Olsson, B., Henser, J., Lundholm, T., Digital twin of a cutting tool, Procedia Cirp, 72, 215-218, 2018.
• 35. Guivarch, D., Mermoz, E., Marino, Y., Sartor, M., Creation of helicopter dynamic systems digital twin using multibody simulations, CIRP Annals, 68(1), 133-136, 2019.
• 36. Guo, J., Zhao, N., Sun, L., Zhang, S., Modular based flexible digital twin for factory design, Journal of Ambient Intelligence and Humanized Computing, 10(3), 1189-1200, 2019.
• 37. Mukherjee, T., DebRoy, T., A digital twin for rapid qualification of 3D printed metallic components, Applied Materials Today, 14, 59-65, 2019.
• 38. Chakshu, N. K., Carson, J., Sazonov, I., Nithiarasu, P., A semi‐active human digital twin model for detecting severity of carotid stenoses from head vibration—A coupled computational mechanics and computer vision method, International journal for numerical methods in biomedical engineering, 35(5), e3180, 2019.
• 39. Shim, C. S., Dang, N. S., Lon, S., Jeon, C. H., Development of a bridge maintenance system for prestressed concrete bridges using 3D digital twin model, Structure and Infrastructure Engineering, 15(10), 1319-1332, 2019.
• 40. Ghosh, A. K., Ullah, A. S., Kubo, A., Hidden Markov model-based digital twin construction for futuristic manufacturing systems, AI EDAM, 33(3), 317-331, 2019.
• 41. Coraddu, A., Oneto, L., Baldi, F., Cipollini, F., Atlar, M., Savio, S., Data-driven ship digital twin for estimating the speed loss caused by the marine fouling, Ocean Engineering, 186, 106063, 2019.
• 42. Bao, J., Guo, D., Li, J., Zhang, J., The modelling and operations for the digital twin in the context of manufacturing, Enterprise Information Systems, 13(4), 534-556, 2019.
• 43. Luo, W., Hu, T., Ye, Y., Zhang, C., Wei, Y., A hybrid predictive maintenance approach for CNC machine tool driven by digital twin, Robotics and Computer-Integrated Manufacturing, 65, 101974, 2020.
• 44. Qian, W., Guo, Y., Cui, K., Wu, P., Fang, W., Liu, D., Multidimensional Data Modeling and Model Validation for Digital Twin Workshop, Journal of Computing and Information Science in Engineering, 21(3), 031005, 2021.
• 45. Suljagic, H., Celebi, N., Obtaining a digital twin of a low-cost robot arm, Proceedings of the 6th International Student Symposium 1- Engineering Sciences, 118-126, 2021.
• 46. White, G., Zink, A., Codecá, L., Clarke, S., A digital twin smart city for citizen feedback, Cities, 110, 103064, 2021.
• 47. Burgos, D., Ivanov, D., Food retail supply chain resilience and the COVID-19 pandemic: A digital twin-based impact analysis and improvement directions, Transportation Research Part E: Logistics and Transportation Review, 152, 102412, 2021.
• 48. Yi, Y., Yan, Y., Liu, X., Ni, Z., Feng, J., Liu, J., Digital twin-based smart assembly process design and application framework for complex products and its case study, Journal of Manufacturing Systems, 58, 94-107, 2021.
• 49. Priyanka, E. B., Thangavel, S., Gao, X. Z., Sivakumar, N. S., Digital twin for oil pipeline risk estimation using prognostic and machine learning techniques, Journal of Industrial Information Integration, 100272, 2021.
• 50. Choi, S. H., Park, K. B., Roh, D. H., Lee, J. Y., Mohammed, M., Ghasemi, Y., Jeong, H., An integrated mixed reality system for safety-aware human-robot collaboration using deep learning and digital twin generation, Robotics and Computer-Integrated Manufacturing, 73, 102258, 2022.
• 51. Lari, K. S., Davis, G. B., Rayner, J. L., Towards a digital twin for characterising natural source zone depletion: A feasibility study based on the Bemidji site, Water Research, 208, 117853, 2022.
• 52. Gao, Y., Chang, D., Chen, C. H., Xu, Z., Design of digital twin applications in automated storage yard scheduling, Advanced Engineering Informatics, 51, 101477, 2022.
• 53. Granacher, J., Nguyen, T. V., Castro-Amoedo, R., Maréchal, F., Overcoming decision paralysis-A digital twin for decision making in energy system design, Applied Energy, 306, 117954, 2022.
• 54. Yang, X., Ran, Y., Zhang, G., Wang, H., Mu, Z., Zhi, S., A digital twin-driven hybrid approach for the prediction of performance degradation in transmission unit of CNC machine tool, Robotics and Computer-Integrated Manufacturing, 73, 102230, 2022.
• 55. Fang, X., Wang, H., Li, W., Liu, G., Cai, B., Fatigue crack growth prediction method for offshore platform based on digital twin, Ocean Engineering, 244, 110320, 2022.