THE DIGITAL TWIN MODEL OF CNC MACHINES TO ESTIMATE OPERATION COMPLETION TIMES

Year 2023, , 303 - 321, 31.08.2023

Elif Cesur , Raşit Cesur , Beyza Nur Aydoğan

https://doi.org/10.46519/ij3dptdi.1215353

Cited By: 1

Abstract

As digital transformation takes hold in the industry, studies are exploring how modeling physical systems in digital environments can boost production efficiency. The objective is to tackle more complex issues than traditional methods and achieve more cost-effective and higher-quality production. The integration of artificial intelligence and machine learning into industrial processes is a very important step in digitalization studies. Integrating artificial intelligence and machine learning using the Internet of Things (IoT) has shown great potential, as data collection, processing, and extraction can be done through a single platform. One of the areas where these technologies are being used is in the Digital Twin (DT) applications. Digital transformation enables real-time control of systems by creating a virtual environment that mirrors the real world. The most effective targets for applying DT technology in industrial control are 3D printers, robots, and CNC benches. In this study, the main objective is to develop a DT model for manufacturing systems. In the second phase of the study, the execution time of linear motion commands on machines of flexible manufacturing systems was estimated using the developed DT model. In the estimation phase, different machine learning algorithms were used and their performances were compared.

Keywords

Digital Twin, İntelligent Manufacturing Systems, CNC Workbenches

CNC TEZGAHLARININ DİJİTAL İKİZ MODELİ İLE KOMUT TAMAMLANMA SÜRELERİNİN TAHMİN EDİLMESİ

Abstract

Endüstride dijital dönüşümün başlamasıyla fiziksel sistemlerin dijital ortamda modellenerek üretim verimliliğinin artması için çok sayıda çalışma yapılmıştır. Bu çalışamalar mevcut yöntemlere kıyasla daha karmaşık sorunları çözmek, maliyet ve kalite açısından daha etkin üretim yapmak amacıyla gerçekleştirilmektedir. Dijitalleşme çalışmalarında yapay zekâ ve makine öğreniminin endüstriyel operasyonlara dahil edilmesi oldukça önemli bir adım olmuştur. IoT ile entegre yapay zekâ ve makine öğrenimi, veri toplama, işleme ve bilgi çıkarımın tek bir yerde yapılmasına izin verdiği için büyük bir potansiyele sahip olduğu görülmüştür. Bu teknolojilerin kullanıldığı alanlardan biri ise Dijital İkiz (Dİ) uygulamalarıdır. Dİ ile, gerçek dünyanın sanal ortamda birebir modeli oluşturularak sistemlerin gerçek zamanlı kontrolü sağlanmaktadır. Endüstriyel kontrolde Dİ teknolojisinin uygulanabileceği en etkin bileşenler ise 3 boyutlu yazıcılar, robotlar ve CNC tezgâhlarıdır. Bu çalışmada, öncelikle üretim sistemlerinin Dİ modelinin geliştirilmesi hedeflenmiştir. Çalışmanın ikinci aşamasında ise geliştirilen Dİ modeli ile esnek imalat sistemi tezgahlarında doğrusal hareket komutlarının tamamlanma süresi tahmin edilmiştir. Tahmin aşamasında birden çok makine öğrenmesi algoritmaları kullanılmış ve performansları karşılaştırılmıştır. 0.995745 R2ve 0.991615 doğruluk değerleri ile Yapay sinir ağları modeli en iyi yöntem olduğu görülmektedir.

Keywords

Dijital İkiz, Zeki İmalat Sistemleri, CNC tezgâhları

References

• 1. Zhang D, “Powering E-Learning In The New Millenium: An Overview of E-Learning and Enabling Technology, Information System Frontiers,” Vol. 5, Issue 2, Page 201–212, 2004.
• 2. [A. Y. C. Nee, S. K. Ong, G. Chryssolouris, and D. Mourtzis, “Augmented reality applications in design and manufacturing,” CIRP Ann Manuf Technol, Vol. 61, Issue 2, Page 657–679, 2012
• 3. D. Ivanov, A. Dolgui, B. Sokolov, F. Werner, and M. Ivanova, “A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0,” Int J Prod Res, Vol. 54, Issue 2, Page 386–402, 2016
• 4. J. E. Brough, M. Schwartz, S. K. Gupta, D. K. Anand, R. Kavetsky, and R. Pettersen, “Towards the development of a virtual environment-based training system for mechanical assembly operations,” Virtual Real, Vol. 11, Issue 4, Page 189–206, 2007
• 5. Blaga and L. Tamas, “Augmented Reality for Digital Manufacturing,” MED 2018 - 26th Mediterranean Conference on Control and Automation, Page 173–178, 2018
• 6. N. Syam and A. Sharma, “Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice,” Industrial Marketing Management, Vol. 69, Issue December 2017, Page 135–146, 2018
• 7. Ramos, J. C. Augusto, and D. Shapiro, “Ambient intelligencethe next step for artificial intelligence,” IEEE Intell Syst, Vol. 23, Page 15–18, 2008
• 8. Duncan, M. Whittington, and V. Chang, “Enterprise security and privacy: Why adding IoT and big data makes it so much more difficult,” Proceedings of 2017 International Conference on Engineering and Technology, ICET 2017, Vol. 2018-Janua, Page 1–7, 2017
• 9. P. Aivaliotis, K. Georgoulias, and K. Alexopoulos, “Using digital twin for maintenance applications in manufacturing: State of the Art and Gap analysis,” Proceedings - 2019 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2019, 2019
• 10. Altamiranda and E. Colina, “A system of systems digital twin to support life time management and life extension of subsea production systems,” OCEANS 2019 - Marseille, OCEANS Marseille 2019, Vol. 2019-June, Page 1–9, 2019
• 11. Murphy et al., “Representing financial data streams in digital simulations to support data flow design for a future Digital Twin,” Robot Comput Integr Manuf, Vol. 61, 2018
• 12. P. Aivaliotis, K. Georgoulias, and G. Chryssolouris, “The use of Digital Twin for predictive maintenance in manufacturing,” Int J Comput Integr Manuf, Vol. 32, Page 1067–1080, 2019
• 13. W. Luo, T. Hu, Y. Ye, C. Zhang, and Y. Wei, “A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin,” Robot Comput Integr Manuf, Vol. 65, Page 101974, 2020
• 14. P. Hehenberger and D. Bradley, “Mechatronic futures: Challenges and solutions for mechatronic systems and their designers,” Mechatronic Futures: Challenges and Solutions for Mechatronic Systems and Their Designers, Page 1–259, 2016
• 15. M. Grieves, “Digital Twin : Manufacturing Excellence through Virtual Factory Replication - A Whitepaper by Dr . Michael Grieves,” White Paper, Issue March, Page 1–7, 2014.
• 16. K. A. Hribernik, L. Rabe, K. D. Thoben, and J. Schumacher, “The product avatar as a product-instance-centric information management concept,” Int J Prod Lifecycle Manag, Vol. 1, Page 367–379, 2006
• 17. J. Ríos, J. C. Hernández, M. Oliva, and F. Mas, “Product avatar as digital counterpart of a physical individual product: Literature review and implications in an aircraft,” Advances in Transdisciplinary Engineering, Vol. 2, Page 657–666, 2015
• 18. T. Wuest, K. Hribernik, and K. D. Thoben, “Accessing servitisation potential of PLM data by applying the product avatar concept,” Production Planning and Control, Vol. 26, Page 1198–1218, 2015
• 19. K. Hribernik, T. Wuest, and K. D. Thoben, “Towards product avatars representing middle-of-life information for improving design, development and manufacturing processes,” IFIP Adv Inf Commun Technol, Vol. 411, Page 85–96, 2013
• 20. M. Shafto et al., “Modeling , Simulation , Information Technology & Processing Roadmap-NASA,” National Aeronautics and Space Administration, Page 1–38, 2012.
• 21. Bilberg and A. A. Malik, “Digital twin driven human–robot collaborative assembly,” CIRP Annals, Vol. 68, Page 499–502, 2019
• 22. S. Meng, S. Tang, Y. Zhu, and C. Chen, “Digital Twin-Driven Control Method for Robotic Automatic Assembly System,” IOP Conf Ser Mater Sci Eng, Vol. 493, 2019
• 23. Q. Liu, H. Zhang, J. Leng, and X. Chen, “Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system,” Int J Prod Res, Vol. 57, Page 3903–3919, 2019
• 24. Botkina, M. Hedlind, B. Olsson, J. Henser, and T. Lundholm, “Digital Twin of a Cutting Tool,” Procedia CIRP, Vol. 72, Page 215–218, 2018
• 25. D. Process, M. Supported, and W. Artificial, “Yapay zeka destekli̇ bi̇r tasarim i̇şlem modeli̇ni̇n yapisi,” Cilt 1, Sayfa 1–8, 2017.
• 26. O. SEVLİ, “3 Boyutlu Baskida KullanilacakMalzemeni̇nMaki̇neÖğrenmesiTekni̇kleri̇İlTahmi̇nlenmesi̇,” International Journal of 3D Printing Technologies and Digital Industry, Cilt 5, Sayı 3, Sayfa 596–605, 2021
• 27. D. ALTUNKAYNAK, B. DUMAN, and K. ÇERİNKAYA, “5 Eksen 3B Yazıcı Tasarımı Ve Uygulaması,” International Journal of 3D Printing Technologies and Digital Industry, Cilt 4, Sayı 2, Sayfa 124–138, 2020
• 28. P. Wang, K. Erkorkmaz, J. McPhee, and S. Engin, “In-process digital twin estimation for high-performance machine tools with coupled multibody dynamics,” CIRP Annals, Vol. 69, issue. 1, Page 321–324, 2020
• 29. K. Liu, L. Song, W. Han, Y. Cui, and Y. Wang, “Time-Varying Error Prediction and Compensation for Movement Axis of CNC Machine Tool Based on Digital Twin,” IEEE Trans Industr Inform, Vol. 18, issue. 1, Page 109–118, 2022
• 30. Y. G. Kabaldin, P. V. Kolchin, D. A. Shatagin, M. S. Anosov, and A. A. Chursin, “Digital Twin for 3D Printing on CNC Machines,” Russian Engineering Research, Vol. 39, issue. 10, Page 848–851, Oct. 2019
• 31. S. Paripooranan, R. Abishek, D. C. Vivek, and S. Karthik, “An Implementation of AR Enabled Digital Twins for 3-D Printing,” Proceedings - 2020 6th IEEE International Symposium on Smart Electronic Systems, iSES 2020, Page 155–160, 2020
• 32. Y. Zhang, L. Mu, G. Shen, Y. Yu, and C. Han, “Fault diagnosis strategy of CNC machine tools based on cascading failure,” J Intell Manuf, Vol. 30, issue. 5, Page 2193–2202, 2019
• 33. Villalonga, G. Beruvides, F. Castaño, R. E. Haber, and M. Novo, “Condition-based Monitoring Architecture for CNC Machine Tools based on Global Knowledge,” IFAC-PapersOnLine, Vol. 51, issue. 11, Page 200–204, 2018
• 34. W. Luo, T. Hu, C. Zhang, and Y. Wei, “Digital twin for CNC machine tool: modeling and using strategy,” J Ambient Intell Humaniz Comput, Vol. 10, issue. 3, Page 1129–1140, 2019
• 35. M. Liu, S. Fang, H. Dong, and C. Xu, “15 Review of digital twin about concepts, technologies, and industrial applications,” J Manuf Syst, Vol. 58, Page 346–361, 2021
• 36. Cimino, E. Negri, and L. Fumagalli, “2 Review of digital twin applications in manufacturing,” Comput Ind, Vol. 113, 2019
• 37. J. Wu, Y. Yang, X. U. N. Cheng, H. Zuo, and Z. Cheng, “3 The Development of Digital Twin Technology Review,” in Proceedings - 2020 Chinese Automation Congress, CAC 2020, Institute of Electrical and Electronics Engineers Inc., Nov. 2020, Page 4901–4906
• 38. R. K. Phanden, P. Sharma, and A. Dubey, “6 A review on simulation in digital twin for aerospace, manufacturing and robotics,” in Materials Today: Proceedings, Elsevier Ltd, 2020, Page 174–178
• 39. M. Holler, F. Uebernickel, and W. Brenner, “Digital Twin Concepts in Manufacturing Industries- A Literature Review and Avenues for further Research,” 18th International Conference on Industrial Engineering (IJIE), Issue 2002, 2016.
• 40. M. Shafto, M. Conroy, R. Doyle, and E. Glaessgen, “DRAFT Modeling, Simulation, information Technology & Processing Roadmap,” Technology Area, 2010.
• 41. M. Madni, C. C. Madni, and S. D. Lucero, “Leveraging digital twin technology in model-based systems engineering,” Systems, Vol. 7, issue. 1, 2019
• 42. Q. Liu, B. Liu, G. Wang, and C. Zhang, “A comparative study on digital twin models,” in AIP Conference Proceedings, 2019.
• 43. J. Leng, D. Wang, W. Shen, X. Li, Q. Liu, and X. Chen, “13 Digital twins-based smart manufacturing system design in Industry 4.0: A review,” Journal of Manufacturing Systems, Vol. 60. Elsevier B.V., Page 119–137, 2021.
• 44. Semeraro, M. Lezoche, H. Panetto, and M. Dassisti, “14 Digital twin paradigm: A systematic literature review,” Comput Ind, Vol. 130, 2021
• 45. M. Grieves, “Digital Twin : Manufacturing Excellence through Virtual Factory Replication,” White Paper, issue. March, 2014.
• 46. R. Zhang, F. Gu, H. Mansaf, T. Wang, and A. D. Ball, “Gear wear monitoring by modulation signal bispectrum based on motor current signal analysis,” Mech Syst Signal Process, Vol. 94, Page 202–213
• 47. M. W. Grieves, “Product lifecycle management: the new paradigm for enterprises,” International Journal of Product Development, Vol. 2, issue. 1–2, 2005
• 48. S. Haag and R. Anderl, “Digital twin – Proof of concept,” Manuf Lett, Vol. 15, 2018
• 49. K. T. Park et al., “Design and implementation of a digital twin application for a connected micro smart factory,” Int J Comput Integr Manuf, Vol. 32, Issue 6, 2019
• 50. Q. Qi, F. Tao, Y. Zuo, and D. Zhao, “Digital Twin Service towards Smart Manufacturing,” in Procedia CIRP, 2018
• 51. Onaji, D. Tiwari, P. Soulatiantork, B. Song, and A. Tiwari, “Digital twin in manufacturing: conceptual framework and case studies,” International Journal of Computer Integrated Manufacturing. Taylor and Francis Ltd., 2022
• 52. M. Mazzoleni, M. Scandella, F. Previdi, and G. Pispola, “Data on the first endurance activity of a Brushless DC motor for aerospace applications,” Data Brief, Vol. 29, Apr. 2020
• 53. Theissler, J. Pérez-Velázquez, M. Kettelgerdes, and G. Elger, “Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry,” Reliab Eng Syst Saf, Vol. 215, 2021
• 54. Y. H. Son, K. T. Park, D. Lee, S. W. Jeon, and S. do Noh, “Digital twin–based cyber-physical system for automotive body production lines,” International Journal of Advanced Manufacturing Technology, Vol. 115, issue. 1–2, 2021
• 55. Biesinger and M. Weyrich, “The Facets of Digital Twins in Production and the Automotive Industry,” in 2019 23rd International Conference on Mechatronics Technology, ICMT 2019, 2019
• 56. M. Lamagna, D. Groppi, M. M. Nezhad, and G. Piras, “A COMPREHENSIVE REVIEW on DIGITAL TWINS for SMART ENERGY MANAGEMENT SYSTEM,” International Journal of Energy Production and Management, Vol. 6, issue. 4, 2021
• 57. E. Onile, R. Machlev, E. Petlenkov, Y. Levron, and J. Belikov, “Uses of the digital twins concept for energy services, intelligent recommendation systems, and demand side management: A review,” Energy Reports, Vol. 7. 2021
• 58. L. Lattanzi, R. Raffaeli, M. Peruzzini, and M. Pellicciari, “12 Digital twin for smart manufacturing: a review of concepts towards a practical industrial implementation,” Int J Comput Integr Manuf, Vol. 34, issue. 6, Page 567–597, 2021
• 59. L. Li, B. Lei, and C. Mao, “Digital twin in smart manufacturing,” J Ind Inf Integr, Vol. 26, 2022
• 60. O. S. Kobyakova, V. I. Starodubov, N. G. Kurakova, and L. A. Tsvetkova, “Digital twins in healthcare: An assessment of technological and practical prospects,” Vestn Ross Akad Med Nauk, Vol. 76, issue. 5, 2021
• 61. K. Bruynseels, F. S. de Sio, and J. van den Hoven, “Digital Twins in health care: Ethical implications of an emerging engineering paradigm,” Front Genet, Vol. 9, issue. FEB, 2018
• 62. L. Pan, X. Guo, Y. Luan, and H. Wang, “Design and realization of cutting simulation function of digital twin system of CNC machine tool,” in Procedia Computer Science, 2021
• 63. H. dos Santos, J. A. B. Montevechi, J. A. de Queiroz, R. de Carvalho Miranda, and F. Leal, “11 Decision support in productive processes through DES and ABS in the Digital Twin era: a systematic literature review,” International Journal of Production Research. Taylor and Francis Ltd., 2021
• 64. Ashtari Talkhestani et al., “An architecture of an Intelligent Digital Twin in a Cyber-Physical Production System,” At-Automatisierungstechnik, Vol. 67, issue. 9, 2019
• 65. J. Wilhelm, C. Petzoldt, T. Beinke, and M. Freitag, “16 Review of Digital Twin-based Interaction in Smart Manufacturing: Enabling Cyber-Physical Systems for Human-Machine Interaction,” Int J Comput Integr Manuf, Vol. 34, issue. 10, Page 1031–1048, 2021
• 66. J. C. P. Cheng, W. Chen, K. Chen, and Q. Wang, “Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms,” Autom Constr, Vol. 112, 2020
• 67. Ashtari Talkhestani et al., “An architecture of an Intelligent Digital Twin in a Cyber-Physical Production System,” At-Automatisierungstechnik, Vol. 67, issue. 9, 2019
• 68. S. Ayvaz and K. Alpay, “Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time,” Expert Syst Appl, Vol. 173, 2021
• 69. Singh, “Big data, industry 4.0 and cyber-physical systems integration: A smart industry context,” in Materials Today: Proceedings, 2021
• 70. M. M. Rathore, S. A. Shah, D. Shukla, E. Bentafat, and S. Bakiras, “The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities,” IEEE Access, Vol. 9. 2021
• 71. W. J. Lee, H. Wu, H. Yun, H. Kim, M. B. G. Jun, and J. W. Sutherland, “Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data,” in Procedia CIRP, Elsevier B.V., 2019, Page 506–511
• 72. Kammerer, M. Gaust, M. Küstner, P. Starke, R. Radtke, and A. Jesser, “Motor Classification with Machine Learning Methods for Predictive Maintenance,” IFAC-PapersOnLine, Vol. 54, issue. 1, Page 1059–1064, 2021
• 73. Gohel, H. Upadhyay, L. Lagos, K. Cooper, and A. Sanzetenea, “Predictive maintenance architecture development for nuclear infrastructure using machine learning,” Nuclear Engineering and Technology, Vol. 52, issue. 7, Page 1436–1442, Jul. 2020
• 74. Q. Zhang, Z. Yang, J. Duan, Z. Liu, and J. Qin, “Three-dimensional visualization interactive system for digital twin workshop,” Journal of Southeast University (English Edition), Vol. 37, issue. 2, 2021
• 75. Z. Han, Y. Li, M. Yang, Q. Yuan, L. Ba, and E. Xu, “Digital twin-driven 3D visualization monitoring and traceability system for general parts in continuous casting machine,” Journal of Advanced Mechanical Design, Systems and Manufacturing, Vol. 14, issue. 7, 2020
• 76. Shi, Q. Bi, and Y. Wang, “Five-axis interpolation of continuous short linear trajectories for 3[PP]S-XY hybrid mechanism by dual Bezier blending,” J Shanghai Jiaotong Univ Sci, Vol. 21, issue. 1, Page 90–102, 2016
• 77. N. Saikumar, N. S. Dinesh, and P. Kammardi, “Experience mapping based prediction controller for the smooth trajectory tracking of DC motors,” Int J Dyn Control, Vol. 5, issue. 3, Page 704–720, 2017
• 78. M. Endo and B. Sencer, “Accurate prediction of machining cycle times by data-driven modelling of NC system’s interpolation dynamics,” CIRP Annals, Vol. 71, issue. 1, Page 405–408, 2022
• 79. T. Gurgenc, F. Ucar, D. Korkmaz, C. Ozel, and Y. Ortac, “A study on the extreme learning machine based prediction of machining times of the cycloidal gears in CNC milling machines,” Production Engineering, Vol. 13, issue. 6, Page 635–647, 2019
• 80. P. Aivaliotis, K. Georgoulias, and G. Chryssolouris, “The use of Digital Twin for predictive maintenance in manufacturing,” Int J Comput Integr Manuf, Vol. 32, issue. 11, 2019
• 81. H. A. Weiss, N. Leuning, K. Hameyer, H. Hoffmann, and W. Volk, “Manufacturing efficient electrical motors with a predictive maintenance approach,” CIRP Annals, Vol. 68, issue. 1, Page 253–256
• 82. El Saddik, “Digital Twins: The Convergence of Multimedia Technologies,” IEEE Multimedia, Vol. 25, issue. 2, Page 87–92, 2018
• 83. G. F. Luger, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Vol. 5th. 2005.
• 84. G. K. Jha, “Artificial Neural Networks - Architectures and Applications,” Artificial Neural Networks - Architectures and Applications, 2013
• 85. S. A. Bini, “Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care?,” Journal of Arthroplasty, Vol. 33, issue. 8, Page 2358–2361, 2018
• 86. D. Naylor, “On the prospects for a (Deep) learning health care system,” JAMA - Journal of the American Medical Association, Vol. 320, issue. 11, Page 1099–1100, 2018
• 87. C.-W. Hsu, C.-C. Chang, and C.-J. Lin, “Propofol and sevoflurane during epidural/general anesthesia: Comparison of early recovery characteristics and pain relief,” Middle East Journal of Anesthesiology, Vol. 17, issue. 5, Page 819–832, 2004.
• 88. R. O. Duda, P. E. Hart, and D. G. Stork, “Pattern classification,” Handbook of Neural Computation, 2004
• 89. Abraham, “Artificial neural networks,” Artificial Neural Networks, Page 1–426, 2011
• 90. Z. Zhou, “R u l e E x t r a c t i o n : U s i n g N e u r a l N e t w o r k s or for N e u r a l Networks ?,” issue. 2, Page 249–253, 2004.
• 91. F. Nielsen, “Recurrent Neural Networks algorithms and applications,” Proceedings - 2021 2nd International Conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2021, Page 38–43, 2021
• 92. Cheng and D. M. Titterington, “[Neural Networks: A Review from Statistical Perspective]: Rejoinder,” Statistical Science, Vol. 9, issue. 1, Page 2–30, 2007
• 93. Cesur Muhammet Raşit and Cesur Elif, “Alcybe CNC Digital Twin” https://github.com/rasitcesur/Alcybe/tree/DigitalTwin/Models/Workbench/DataSets.
• 94. DMG MORI, “What is the Digital Twin?,” 2023. https://dk.dmgmori.com/news-and-media/blog-and-stories/blog/what-is-the-digital-twin (accessed Aug. 16, 2023).