- Open Access
- Article
Advanced Digital Twin of a Industrial Robotic System for Measuring Pipe Wall Thickness in Nuclear Power Plants
by Rogério Adas Pereira Vitalli 1 and João Manoel Losada Moreira 2
1 Rogério Adas Pereira Vitalli, Federal University of ABC (UFABC), Interdisciplinary Laboratory of Nuclear Energy (NUC-LAB), Santo André, São Paulo, 09210-580, Brazil
2 João Manoel Losada Moreira, Federal University of ABC (UFABC), Interdisciplinary Laboratory of Nuclear Energy (NUC-LAB), Santo André, São Paulo, 09210-580, Brazil
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 3, Issue 12, Page # 14-23, 2024; DOI: 10.55708/js0312002
Keywords: Digital Twin, Ultrasound Measurement, Pipe Wall Thickness, Virtual Commissioning, Process Simulate
Received: 15 September 2023, Revised: 30 October, 2024, Accepted: 23 November, 2024, Published Online: 22 December, 2024
(This article belongs to the Special Issue Special Issue on Multidisciplinary Sciences and Advanced Technology 2024 & Section Biochemical Research Methods (BRM))
APA Style
Adas, R., Vitalli, P., Manoel, J., & Moreira, L. (2024). Advanced digital twin of an industrial robotic system for measuring pipe wall thickness in nuclear power plants. Journal of Engineering Research and Sciences, 3(12), 14–23. https://doi.org/10.55708/js0312002
Chicago/Turabian Style
Adas, Rogério, Pereira Vitalli, João Manoel, and Losada Moreira. “Advanced Digital Twin of an Industrial Robotic System for Measuring Pipe Wall Thickness in Nuclear Power Plants.” Journal of Engineering Research and Sciences 3, no. 12 (2024): 14–23. https://doi.org/10.55708/js0312002.
IEEE Style
R. Adas, P. Vitalli, J. Manoel, and L. Moreira, “Advanced digital twin of an industrial robotic system for measuring pipe wall thickness in nuclear power plants,” Journal of Engineering Research and Sciences, vol. 3, no. 12, pp. 14–23, 2024, doi: 10.55708/js0312002.
This paper presents the development of the digital twin of an advanced industrial robotic system for pipe wall thickness inspection in the turbine building of nuclear power plants. The robotic inspection system consists of 3 units, the first being the mobile unit, the second a robotic unit for automatic pipe wall thickness measurement using the ultrasound technique, and the third unit for power supply, communication and auxiliary services. To develop the advanced robotic inspection system, a prototype was built in the laboratory to study different situations and geometric configurations that may arise in the field (turbine building). With Process Simulate software version 16.1.2, the digital twin of the prototype was developed including industrial robot, metal platform and pipe sections. This paper presents the results for the virtual commissioning of the wall thickness measurement of a vertical pipe section and the novel design of the End-Effector of the industrial robot. Therefore, technical discussions are made on the requirements to deal from the design to the functional requirements of developing the end-effector, digital twin and the creation of a commissioning method for industrial robots. The analyses and insights from virtual commissioning made it possible to verify that the robot could access any part of the pipe surface through interpolated movements with spatial coordinates of the robotic arm along its sides.
- USNRC-NUREG–2191. “Generic Aging Lessons Learned for Subsequent License Renewal (GALL-SLR) Report”, Final Report Vol. 2. US Nuclear Regulatory Commission, 2017.
- IAEA-SRS 82. Ageing management for nuclear power plants: International generic ageing lessons learned (IGALL), Rev. 1. International Atomic Energy Agency, 2020.
- S. A. Cancemi; R. L. Frano; Preliminary study of the effects of ageing on the long-term performance of NPP pipe. Progress in Nuclear Energy, 131, 103573, 2021.
- Q. Zhang; Y. Li; E. Lim; J. Sun. Real Time Object Detection in Digital Twin with Point-Cloud Perception for a Robotic Manufacturing Station. Proceedings of the 27th International Conference on Automation & Computing, University of the West of England, Bristol, UK, 1-3 September 2022.
- H. Zhou; S. Zhang; J. Zhang; C. Zhang; S. Wang; Y. Zhai; W. Li. Design, development, and field evaluation of a rubber tapping robot. Journal of Field Robotics, 39, 28–54, 2021.
- Tugal, H.; Cetin, K.; Petillot, Y.; Dunnigan, M.; Erden, M. S. Contact-based object inspection with mobile manipulators at near- optimal base locations. Robotics and Autonomous Systems, 161, 104345, 2023.
- J. Barbosa, P. Leitão, E. Adam and D. Trentesaux, “Dynamic self- organization in holonic multi-agent manufacturing systems: The ADACOR evolution,” Computers in industry, v. 66, p. 99-111, 2015.
- J. Barata, L. Camarinha-Matos and M. Onori, “A multi-agent-based control approach for evolvable assembly systems,” INDIN’05. 2005 3rd IEEE International Conference on Industrial Informatics, pp. 478-483, August 2005.
- E. Trunzer, A. Calà, P. Leitão, M. Gepp, J. Kinghorst, A. Lüder et. al., “System architectures for industrie 4.0 applications,” Production Engineering, v. 13, n. 3, p. 247-257, 2019.
- Boschert, S.; Rosen, R. Digital Twin—The Simulation Aspect. In: Hehenberger, P.; Bradley, D. (Editors), Mechatronic Futures – Challenges and Solutions for Mechatronic Systems and their Designers. pag. 59-74, Spring, 2016.
- Bratchikov,S.; Abdullin, A.; Demidova, G. L.; Lukichev, D. V. Development of Digital Twin for Robotic Arm. 2021 IEEE 19th International Power Electronics and Motion Control Conference (PEMC) DOI: 10.1109/PEMC48073.2021.9432535, 2021.
- Nekoo, S. R.; Acosta, J. A.; Heredia, G.; Ollero, A. A benchmark mechatronics platform to assess the inspection around pipes with variable pitch quadrotor for industrial sites. Mechatronics 79, 102641, 2021.
- Lee, D. et al. Digital twin-driven deep reinforcement learning for adaptive task allocation in robotic construction. Advanced Engineering Informatics, v. 53, 1 ago. 2022.
- Gartner Top 10 Strategic Technology Trends For 2018. Disponível em <https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2018>. Acesso em: 20 fev . 2023.
- What Is Digital Twin Technology – And Why Is It So Important? Disponívelem:..<https://www.forbes.com/sites/bernardmarr/2017/03/06/what-is-digital-twin-technology-and-why-is-it-so-important/?sh=1365c81c2e2a>. Acesso em: 20 fev. 2023.
- Tugal, H.; Cetin, K.; Petillot, Y.; Dunnigan, M.; Erden, M. S. Contact-based object inspection with mobile manipulators at near-optimal base locations. Robotics and Autonomous Systems, 161, 104345, 2023.
- Bratchikov,S.; Abdullin, A.; Demidova, G. L.; Lukichev, D. V. Development of Digital Twin for Robotic Arm. 2021 IEEE 19th International Power Electronics and Motion Control Conference (PEMC) DOI: 10.1109/PEMC48073.2021.9432535, 2021.
- Nekoo, S. R.; Acosta, J. A.; Heredia, G.; Ollero, A. A benchmark mechatronics platform to assess the inspection around pipes with variable pitch quadrotor for industrial sites. Mechatronics 79, 102641, 2021.
- Qiu, B. et al. A Feasible Method for Evaluating Energy Consumption of Industrial Robots. Proceedings of the 16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021. Anais…Institute of Electrical and Electronics Engineers Inc., 1 ago. 2021a.
- Lee, D. et al. Digital twin-driven deep reinforcement learning for adaptive task allocation in robotic construction. Advanced Engineering Informatics, v. 53, 1 ago. 2022.
- Partiksha; K., A. Robotic Tele-operation Performance Analysis via Digital Twin Simulations. 2022 14th International Conference on COMmunication Systems and Networks, COMSNETS 2022. Anais…Institute of Electrical and Electronics Engineers Inc., 2022.
- Fan, S. et al. A new approach to enhance the stiffness of heavy-load parallel robots by means of the component selection. Robotics and Computer-Integrated Manufacturing, v. 61, 1 fev. 2020.
- Huynh, H. N. et al. Modelling the dynamics of industrial robots for milling operations. Robotics and Computer-Integrated Manufacturing, v. 61, 1 fev. 2020.
- Vitalli, R. Método de abordagem e estrutura geral do projeto de pesquisa desenvolvimento da RSTM. Documento Número: UFABC- ROBOT-001-Rev00, 2023.
- Partiksha; K., A. Robotic Tele-operation Performance Analysis via Digital Twin Simulations. 2022 14th International Conference on Communication Systems and Networks, COMSNETS 2022. Anais…Institute of Electrical and Electronics Engineers Inc., 2022.
- Fan, S. et al. A new approach to enhance the stiffness of heavy-load parallel robots by means of the component selection. Robotics and Computer-Integrated Manufacturing, v. 61, 1 fev. 2020.
- Huynh, H. N. et al. Modelling the dynamics of industrial robots for milling operations. Robotics and Computer-Integrated Manufacturing,v. 61, 1 fev. 2020.
- SIEMENS PLM, Process Simulate. Germany. Siemens Product Lifecycle Management Software. 2018. Document ID: MT45215. Version: 15.1.2.
- ABB, Product manual IRB 1600/1660. Sweden. ABB Robotics. 06/01/2022. Document ID: 3HAC026660-001. Revision: AH.
- Wildgrube F, Perzylo A, Rickert M, et al. Semantic mates: Intuitive geometric constraints for efficient assembly specifications. 2023 IEEE/ RSJ International Conference on Intelligent Robots and Systems (IROS); 2023.
- Rauen H. Industrie 4.0 in practice – Solutions for industrial applications. Frankfurt: VDMA Industrie 4.0 Forum; 2023.
- Karaman S, Frazzoli E. Sampling-based algorithms for optimal motion planning. The International Journal of Robotics Research.2024;30(7):846–894.