Malzeme Mühendisliğinde Gelişmiş Malzeme Davranışı Tahmini ve Üretim Süreci Optimizasyonu için Dijital İkizler ve Uygulamaları

Öz

Endüstri 4.0'ın gelişi ve dijital devrim, malzeme mühendisliğini yeniden tanımlama potansiyeline sahip dijital ikizler gibi yenilikçi teknolojileri ortaya çıkardı. Fiziksel varlıkların sanal temsilleri olan dijital ikizler, malzeme davranışını modelleyebilir ve tahmin edebilir, malzemelerin gelişmiş tasarımını, test edilmesini ve üretilmesini sağlar. Bununla birlikte, malzeme mühendisliğinde tahmine dayalı analiz ve süreç optimizasyonu için dijital ikizlerin kapsamlı kullanımı büyük ölçüde keşfedilmemiş durumda bulunmaktadır. Bu araştırma, dijital ikizlerin malzeme davranışını tahmin etme ve üretim süreçlerini optimize etme konusundaki yeteneklerini araştırarak, bu ilgi çekici kesişimi derinlemesine incelemeyi ve böylece gelişmiş malzeme üretiminin gelişimine katkıda bulunmayı amaçlamaktadır. Çalışmamız, dijital ikiz kavramının ve bunların malzeme mühendisliğindeki özel uygulamalarının ayrıntılı bir araştırması ile başlayacak ve sanal bir ortamda karmaşık malzeme davranışlarını ve süreçlerini simüle etme yeteneklerini vurgulayacaktır. Daha sonra, mekanik özellikler, arıza modları ve faz dönüşümleri gibi çeşitli malzeme davranışlarını tahmin etmek için dijital ikizlerden yararlanmaya odaklanacağız ve dijital ikizlerin sonuçları doğru bir şekilde tahmin etmek için tarihsel veriler, gerçek zamanlı izleme ve gelişmiş algoritmaların bir kombinasyonunu nasıl kullanabileceğini göstereceğiz. Ayrıca, dijital ikizlerin döküm, işleme ve eklemeli imalat dahil olmak üzere malzeme üretim süreçlerini optimize etmedeki rolüne değinerek dijital ikizlerin bu süreçleri nasıl modelleyebileceğini, olası sorunları nasıl tanımlayabileceğini ve en uygun parametreleri nasıl önerebileceğini göstereceğiz. Avantajları ve zorlukları da dahil olmak üzere malzeme mühendisliğinde dijital ikizlerin uygulanmasına ilişkin pratik bilgiler sağlamak için ayrıntılı vaka çalışmaları sunacağız. Araştırmamızın son bölümü, veri kalitesi, model doğrulama ve hesaplama talepleri gibi dijital ikizlerin uygulanmasındaki mevcut zorlukları ele alacak, potansiyel çözümler önerecek ve gelecekteki yönleri özetleyecektir. Bu araştırma, malzeme mühendisliğinde dijital ikizlerin dönüştürücü potansiyelinin altını çizmeyi ve böylece daha verimli, sürdürülebilir ve akıllı malzeme tasarımı ve üretim süreçlerinin önünü açmayı amaçlamaktadır.

Anahtar Kelimeler

• Dijital İkiz
• Endüstri 4.0
• Proses Optimizasyonu
• İleri Malzemeler
• Malzeme Davranış Tahmini

Harnessing the Power of Digital Twins for Enhanced Material Behavior Prediction and Manufacturing Process Optimization in Materials Engineering

Abstract

The advent of Industry 4.0 and the digital revolution have brought forth innovative technologies such as digital twins, which have the potential to redefine the landscape of materials engineering. Digital twins, virtual representations of physical entities, can model and predict material behavior, enabling enhanced design, testing, and manufacturing of materials. However, the comprehensive utilization of digital twins for predictive analysis and process optimization in materials engineering remains largely uncharted. This research intends to delve into this intriguing intersection, investigating the capabilities of digital twins in predicting material behavior and optimizing manufacturing processes, thereby contributing to the evolution of advanced materials manufacturing. Our study will commence with a detailed exploration of the concept of digital twins and their specific applications in materials engineering, emphasizing their ability to simulate intricate material behaviors and processes in a virtual environment. Subsequently, we will focus on exploiting digital twins for predicting diverse material behaviors such as mechanical properties, failure modes, and phase transformations, demonstrating how digital twins can utilize a combination of historical data, real-time monitoring, and sophisticated algorithms to predict outcomes accurately. Furthermore, we will delve into the role of digital twins in optimizing materials manufacturing processes, including casting, machining, and additive manufacturing, illustrating how digital twins can model these processes, identify potential issues, and suggest optimal parameters. We will present detailed case studies to provide practical insights into the implementation of digital twins in materials engineering, including the advantages and challenges. The final segment of our research will address the current challenges in implementing digital twins, such as data quality, model validation, and computational demands, proposing potential solutions and outlining future directions. This research aims to underline the transformative potential of digital twins in materials engineering, thereby paving the way for more efficient, sustainable, and intelligent material design and manufacturing processes.

Keywords

• Digital Twins
• Industry 4.0
• Process Optimization
• Material Behavior Prediction
• Advanced Materials

Kaynakça

1. [1] A. Thelen, et al., "A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies," Structural and Multidisciplinary Optimization, vol. 65, no. 12, pp. 354, 2022.
2. [2] T. Pasang, et al., "Additive manufacturing of titanium alloys–Enabling re-manufacturing of aerospace and biomedical components," Microelectronic Engineering, vol. 270, p. 111935, 2023.
3. [3] Y. Wang, et al., "Digital-Twin-Enhanced Quality Prediction for the Composite Materials," Engineering, 2023.
4. [4] Y. Wang, et al., "A survey on digital twins: architecture, enabling technologies, security and privacy, and future prospects," IEEE Internet of Things Journal, 2023.
5. [5] A. Cheloee Darabi, et al., "Hybrid Data-Driven Deep Learning Framework for Material Mechanical Properties Prediction with the Focus on Dual-Phase Steel Microstructures," Materials, vol. 16, no. 1, p. 447, 2023.
6. [6] M. Javaid and A. Haleem, "Digital Twin applications toward Industry 4.0: A Review," Cognitive Robotics, 2023.
7. [7] L. Gardner, "Metal additive manufacturing in structural engineering–review, advances, opportunities and outlook," Structures, vol. 47, 2023.
8. [8] N. Apostolakis, et al., "Digital Twins for Next-Generation Mobile Networks: Applications and Solutions," IEEE Communications Magazine, 2023.

9. [9] L. Shimin, J. Bao, and P. Zheng, "A review of digital twin-driven machining: From digitization to intellectualization," Journal of Manufacturing Systems, vol. 67, pp. 361-378, 2023.
10. [10] J. Jin, et al., "A Digital Twin system of reconfigurable tooling for monitoring and evaluating in aerospace assembly," Journal of Manufacturing Systems, vol. 68, pp. 56-71, 2023.
11. [11] G. Albuquerque, et al., "Digital Twins as Foundation for Augmented Reality Applications in Aerospace," in Springer Handbook of Augmented Reality, Cham: Springer International Publishing, 2023, pp. 881-900.
12. [12] S. M. Hossain, et al., "A New Era of Mobility: Exploring Digital Twin Applications in Autonomous Vehicular Systems," arXiv preprint arXiv:2305.16158, 2023.
13. [13] Z. Lv, H. Lv, and M. Fridenfalk, "Digital Twins in the Marine Industry," Electronics, vol. 12, no. 9, pp. 2025, 2023.- F. Mauro and A. A. Kana, "Digital twin for ship life-cycle: A critical systematic review," Ocean Engineering, vol. 269, p. 113479, 2023.
14. [14] S. de López Diz, et al., "A real-time digital twin approach on three-phase power converters applied to condition monitoring," Applied Energy, vol. 334, p. 120606, 2023.
15. [15] R. Kaul, et al., "The role of AI for developing digital twins in healthcare: The case of cancer care," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 13, no. 1, pp. e1480, 2023.
16. [16] T. Sun, X. He, and Z. Li, "Digital twin in healthcare: Recent updates and challenges," Digital Health, vol. 9, pp. 20552076221149651, 2023.
17. [17] O. C. Madubuike and C. J. Anumba, "Digital Twin–Based Health Care Facilities Management," Journal of Computing in Civil Engineering, vol. 37, no. 2, pp. 04022057, 2023.
18. [18] S. Cesco, et al., "Smart agriculture and digital twins: Applications and challenges in a vision of sustainability," European Journal of Agronomy, vol. 146, p. 126809, 2023.
19. [19] T. Bergs, et al., "Digital twins for cutting processes," CIRP Annals, 2023.
20. [20] P. Stavropoulos, et al., "Metamodelling of manufacturing processes and automation workflows towards designing and operating digital twins," Applied Sciences, vol. 13, no. 3, pp. 1945, 2023.
21. [21] C. Zhang, et al., "A digital twin defined autonomous milling process towards the online optimal control of milling deformation for thin-walled parts," The International Journal of Advanced Manufacturing Technology, vol. 124, no. 7-8, pp. 2847-2861, 2023.
22. [22] Zhang Haolin, et al., "Research on Construction of Drilling Digital Twin System," 石油钻探技术, vol. 51, pp. 1-8, 2023.
23. [23] F. Hu, et al., "Digital twin-based decision making paradigm of raise boring method," Journal of Intelligent Manufacturing, vol. 34, no. 5, pp. 2387-2405, 2023.
24. [24] C. Wang, I.-S. Fan, and S. King, "A Review of Digital Twin for Vehicle Predictive Maintenance System," 2023.
25. [25] M. Grieves, "Digital twin: manufacturing excellence through virtual factory replication," White Paper, vol. 1, 2014, pp. 1-7.
26. [26] M. Grieves and J. Vickers, "Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems," Complexity, 2017.
27. [27] S. R. Kalidindi, M. Buzzy, B. L. Boyce, and R. Dingreville, "Digital twins for Materials," Front. Mater., vol. 9, p. 48, 2022.
28. [28] X. Li, F. Tao, Y. Cheng, and Z. Yin, "Digital twin-driven product design, manufacturing and service with big data," Int. J. Adv. Manuf. Technol., vol. 92, no. 5-8, pp. 2155-2167, 2017.
29. [29] L. Zhang et al., "Digital twins for additive manufacturing: a state-of-the-art review," Appl. Sci., vol. 10, no. 23, p. 8350, 2020.
30. [30] Y. Lu, C. Liu, I. Kevin, K. Wang, H. Huang, and X. Xu, "Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues," Robot. Comput. Integr. Manuf., vol. 61, p. 101837, 2020.
31. [31] F. Tao, H. Zhang, A. Liu, and A. Y. Nee, "Digital twin in industry: State-of-the-art," IEEE Trans. Ind. Inform., vol. 15, no. 4, pp. 2405-2415, 2018.
32. [32] Y. Wang, J. Wan, D. Zhang, D. Li, and C. Zhang, "Towards smart factory for Industry 4.0: a self-organized multi-agent system with big data based feedback and coordination," Comput. Networks, vol. 101, pp. 158-168, 2016.
33. [33] W. Yu, P. Patros, B. Young, E. Klinac, and T. G. Walmsley, "Energy digital twin technology for industrial energy management: Classification, challenges and future," Renewable Sustainable Energy Rev., vol. 161, p. 112407, 2022.
34. [34] Erkan Tur, Ü. Aytar and Y. Sarıkavak, "Main and Critical Differences (Microstructure and Tensile Properties) Between Electron Beam Melted and Selective Laser Melted Ti-6Al-4V," in Turkish Aerospace Industries Lift Up 2021-2022 Proceedings Book, Ankara, Turkey, 2022, pp. 262-267. ISBN: 978-605-65722-5-8.
35. [35] S. Liu, et al., "Digital twin modeling method based on biomimicry for machining aerospace components," Journal of Manufacturing Systems, vol. 58, pp. 180-195, 2021.
36. [36] T. Mukherjee and T. DebRoy, "A digital twin for rapid qualification of 3D printed metallic components," Applied Materials Today, vol. 14, pp. 59-65, 2019.
37. [37] A. Phua, C. H. J. Davies, and G. W. Delaney, "A digital twin hierarchy for metal additive manufacturing," Computers in Industry, vol. 140, p. 103667, 2022.
38. [38] L. Zhang, et al., "Digital twins for additive manufacturing: a state-of-the-art review," Applied Sciences, vol. 10, no. 23, pp. 8350, 2020.
39. [39] D. R. Gunasegaram, et al., "The case for digital twins in metal additive manufacturing," Journal of Physics: Materials, vol. 4, no. 4, pp. 040401, 2021.
40. [40] S. Gopalakrishnan, N. W. Hartman, and M. D. Sangid, "Model-based feature information network (mfin): a digital twin framework to integrate location-specific material behavior within component design, manufacturing, and performance analysis," Integrating Materials and Manufacturing Innovation, vol. 9, pp. 394-409, 2020.
41. [41] Z. Liu, et al., "Intelligent prediction method for operation and maintenance safety of prestressed steel structure based on digital twin technology," Advances in Civil Engineering, vol. 2021, pp. 1-17, 2021.
42. [42] T. Bergs, et al., "The concept of digital twin and digital shadow in manufacturing," Procedia CIRP, vol. 101, pp. 81-84, 2021.