Digital Twin in manufacturing. Part II. Case studies in removal and additive machining processes
Cyfrowy bliźniak w procesach wytwórczych. Część II. Przykłady zastosowań w skrawaniu i obróbce przyrostowej *
Mechanik nr 02/2023 - Nowe technologie
ABSTRACT: In this paper, a survey of the applications of digital twins (DTs) in removal and additive machining operations performed in smart manufacturing is presented. Some representative examples of virtual modelling in these manufacturing areas at different scales and complexity, including tools, fixtures, machines, equipment and manufacturing/production systems are presented and overviewed. Current experiences of research centres and machine tools companies, which develop and implement of DT technology in the context of control and optimization of machining processes performed on CNC machine tools, are highlighted. According to the author’s opinion this comprehensive survey would encourage to undertake this important manufacturing problem to implement new virtual tools for developing the I4.0 strategy.
KEYWORDS: manufacturing, Industry 4.0, smart manufacturing, Internet of Things, digital twin
STRESZCZENIE: W artykule zaprezentowano przegląd zastosowań cyfrowego bliźniaka (DT) w skrawaniu i obróbce przyrostowej. Podano przykłady o różnym poziomie złożoności, z uwzględnieniem oprzyrządowania, maszyn, procesów i systemów wytwórczych/produkcyjnych. Omówiono doświadczenia ośrodków badawczych we wdrażaniu technologii DT w kontekście sterowania procesami obróbkowymi na obrabiarkach CNC i ich optymalizacji. Zdaniem autora artykuł powinien zachęcić do podjęcia problematyki wprowadzania nowych narzędzi informatycznych w rozwoju strategii Przemysłu 4.0.
SŁOWA KLUCZOWE: przemysł wytwórczy, Przemysł 4.0, inteligentne wytwarzanie, internet rzeczy, cyfrowy bliźniak
BIBLIOGRAFIA / BIBLIOGRAPHY:
[1] Grzesik W. „Digital twin in manufacturing. Part I. State of the art, architecture and applications [Cyfrowy bliźniak w procesach wytwórczych. Część I. Stan zagadnienia, architektura i zastosowania]”. Mechanik. 1 (2023): 8–13, https://doi.org/10.17814/mechanik.2023.1.1.
[2] „Digital production planning and virtual commissioning”, https://new.siemens.com/global/ en/markets/automotive-manufacturing/digital-twin-production.html (dostęp: listopad 2022).
[3] Qi Q., Tao F., Hu T., Anwer N., Liu A., Wei Y., Wang L., Nee A. “Enabling technologies and tools for digital twin”. Journal of Manufacturing Systems. 58 B (2021): 3–21, https://doi.org/10.1016/j.jmsy.2019.10.001.
[4] Warke V., Kumar S., Bongale A., Kotecha K. “Sustainable development of smart manufacturing driven by the digital twin framework: a statistical analysis”. Sustainability. 13 (2021): 10139, https://doi.org/10.3390/su131810139.
[5] Yang D., Karimi H., Kaynak O., Yin S. “Developments of digital twin technologies in industrial, smart city and healthcare sectors: a survey”. Complex Engineering Systems. 1:3 (2021): https://doi.org/10.20517/ces.2021.06.
[6] Mihai S., Davis W., Hung D.V., Trestian R., Karamanoglu M., Barn B., Prasad R.V., Venkataraman H., Nguyen H. “A Digital Twin Framework for Predictive Maintenance in Industry 4.0”. Conference paper. Middlesex University Research Repository (2021): https://www.researchgate.net/publication/348629312.
[7] Cimino Ch., Negri E., Fumagalli L. “Review of digital twin applications in manufacturing”. Computers in Industry. 113 (2019): 103130, https://doi.org/10.1016/j.compind.2019.103130.
[8] “Digital Twin Applications in Manufacturing”, https://imr.ie/2021/01/11/digital-twin- applications-in-manufacturing/ (dostęp: listopad 2022).
[9] Afazov S., Scrimieri D. “Chatter model for enabling a digital twin in machining”. International Journal of Advanced Manufacturing Technology. 110 (2020): 2439–2444, https://doi.org/10.1007/s00170-020-06028-9.
[10] Rojek I., Mikołajewski D., Dostatni E. ”Digital Twins in Product Lifecycle for Sustainability in Manufacturing and Maintenance”. Applied Science MDPI. 11, 31 (2021): https://dx.doi.org/app11010031.
[11] Hardwick M. “Assessment of Digital Twin manufacturing frameworks”, https://www.nist.gov (dostęp: listopad 2022).
[12] Raza M., Kumar P.M., Viet Hung D., Davis W., Nguyen H., Trestian R. “A Digital Twin Framework for Industry 4.0 Enabling Next-Gen Manufacturing”. 2020 9th International Conference on Industrial Technology and Management (ICITM). (2020): 73–77, https://doi.org/10.1109/ICITM48982.2020.9080395.
[13] Huang Z., Shen Y., Li J., Fey M., Brecher Ch. “A survey on AI-driven Digital Twins in Industry 4.0: smart manufacturing and advanced robotics. Sensors MDPI. 21 (2021): 6340, https://doi.org/10.3390/s21196340.
[14] Luo W., Hu T., Zhang Ch., WeiY. “Digital twin for CNC machine tool: modeling and using strategy”. Journal of Ambient Intelligence and Humanized Computing. 10 (2018): 1129–1140, https://doi.org/10.1007/s12652--018-0946-5.
[15] Bergs T., Gierlings S., Auerbach T., Klink A., Schraknepper D., Augspurger T. “The concept of digital twin and digital shadow in manufacturing”. Procedia CIRP. 101 (2021): 80–84, https://doi.org/10.1016/j.procir.2021.02.010.
[16] Zhang X., Zhu W. “Application framework of digital twin-driven product smart manufacturing system: a case study of aeroengine blade manufacturing”. International Journal of Advanced Robotic Systems. 16 (5) (September-October 2019): 1–16, https://doi.org/10.1177/1729881419880663.
[17] Shao G., Helu M. “Framework for a digital twin in manufacturing: Scope and requirements”. Manufacturing Letters. 24 (2020): 105–107, https://doi.org/10.1016/j.mfglet.2020.04.004.
[18] Jarosz K., Ozel T. “Machine learning approaches towards digital twin development for machining systems”. International Journal of Mechatronics and Manufacturing Systems, 15/2-3 (2022): 127–147, https://doi.org/10.1504/IJMMS.2022.124922.
[19] Ma X., Tao F., Zhang M., Wang T., Zuo Y. “Digital twin enhanced human-machine interaction in product lifecycle”. Procedia CIRP. 83 (2019): 789–793, https://doi.org/10.1016/j.procir.2019.04.330.
[20] Grzesik W., Niesłony P., Kiszka P. „Programowanie obrabiarek CNC ”. Warszawa: PWN (2020).
[21] Grzesik W. „Podstawy skrawania materiałów konstrukcyjnych”. Warszawa: PWN (2018).
[22] Bartoszuk M., Grzesik W., Chudy R. „Zastosowanie wielosensorowego systemu pomiarowego do monitorowania procesu skrawania – przypadek sekwencyjnych procesów toczenia i nagniatania”. Mechanik. 1 (2021): 6–12, https://doi.org/10.17814/mechanik.2021.1.1.
[23] Grzesik W., Żak K., Niesłony P., Chudy R. „Detection of the occurrence of cutting vibration in surface profiles generated in CBN precision hard turning”. Procedia CIRP. 71 (2018): 89–92, https://doi.org/10.1016/j.procir.2018.05.077.
[24] ISO 23247 – Automation systems and integration – Digital Twin Framework for Manufacturing, https://www.iso.org/obp/ui/#iso:std:iso:23247.
[25] Zhang L., Chen X., Zhou W., Cheng T., Chen L., Guo Z., Han B., Lu L. “Digital Twins for Additive Manufacturing: A State-of-the-Art Review”. Applied Science MDPI. 10 (2020): 8350, doi.org/10.3390/app10238350.
[26] Mukherjee T., DebRoy T. “A digital twin for rapid qualification of 3D printed metallic components”. Applied Materials Today. 14 (2019): 59–65: https://doi.org/10.1016/j.apmt.2018.11.003.
[27] Grzesik W., Ruszaj A. Hybrydowe metody obróbki materiałów konstrukcyjnych. Warszawa: PWN (2021).
DOI: https://doi.org/10.17814/mechanik.2023.2.4
* Artykuł recenzowany
