Gemelos funcionales para validar el software de control

  1. Álvarez, María Luz 1
  2. Sarachaga, Isabel 1
  3. Burgos, Arantzazu 1
  4. Iriondo, Nagore 1
  1. 1 Universidad del País Vasco/Euskal Herriko Unibertsitatea
    info

    Universidad del País Vasco/Euskal Herriko Unibertsitatea

    Lejona, España

    ROR https://ror.org/000xsnr85

Revista:
Revista iberoamericana de automática e informática industrial ( RIAI )

ISSN: 1697-7920

Año de publicación: 2024

Volumen: 21

Número: 2

Páginas: 159-170

Tipo: Artículo

DOI: 10.4995/RIAI.2024.20830 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Revista iberoamericana de automática e informática industrial ( RIAI )

Resumen

La innovación y los retos tecnológicos de la fabricación inteligente han provocado un incremento notable en la complejidad del software de control de los sistemas de producción automatizados (aPS) integrados en un entorno global interconectado. Una herramienta de pruebas muy potente para su validación es emplear plantas virtuales (uno de los pilares de la digitalización en la industria). En este contexto, este artículo contribuye con una metodología de diseño e implementación de gemelos funcionales construidos a partir de componentes funcionales básicos de librería, que no precisa herramientas comerciales de desarrollo de plantas virtuales. Como representación virtual de la funcionalidad de una entidad del sistema de producción, el gemelo funcional se empleará como herramienta de pruebas para probar la reacción del sistema de control tanto en producción normal como ante la inyección de fallos. La metodología se ha aplicado en la construcción de los gemelos funcionales que permiten validar el sistema de control de una célula de ensamblaje.

€ Ver financiación

Información de financiación

Financiadores

Referencias bibliográficas

  • Adamenko, D., Kunnen, S,. Pluhnau, R., Loibl, A., Nagarajah, A., 2020. Review and comparison of the methods of designing the Digital Twin. Procedia CIRP, 91, 27-32. https://doi.org/10.1016/j.procir.2020.02.146
  • ADEPA (Agence nationale pour le DEveloppment de la Production Automatisée), 1981. GEMMA (Guide d'Étude des Modes de Marches et d'Arrêts).
  • Aivaliotis, P., Georgoulias, K., Arkouli, Z., Makris, S., 2019. Methodology for enabling Digital Twin using advanced physics-based modelling in predictive maintenance. Procedia CIRP, 81, 417-422. https://doi.org/10.1016/j.procir.2019.03.072
  • Álvarez, M.L., Estévez, E., Sarachaga, I., Burgos, A. Marcos, M., 2013. A novel approach for supporting the development cycle of automation systems. International Journal of Advanced Manufacturing Technology, 68, 711-725. https://doi.org/10.1007/s00170-013-4793-4
  • Álvarez, M.L., Sarachaga, I., Burgos, A., Iriondo, N., 2023. Gemelo funcional para pruebas del software de sistemas de producción automatizados. XLIV Jornadas de Automática, 831-836. https://doi.org/10.17979/spudc.9788497498609.831
  • AutomationML, 2023. [Website] https://www.automationml.org/
  • Barbieri, G., Bertuzzi, A., Capriotti, A., Ragazzini, L., Gutierrez, D., Negri, E., Fumagalli, L., 2021.A virtual commissioning based methodology to integrate digital twins into manufacturing systems. Production Engineering, 15, 397-412. https://doi.org/10.1007/s11740-021-01037-3
  • Burgos, A., Iriondo, N., Álvarez, M.L., Sarachaga, I., 2021. MeiA 4.0 para abordar los retos actuales de formación en automatización. XLII Jornadas de Automática, 1-3 Sep., Castellón, España, 240-247. https://doi.org/10.17979/spudc.9788497498043.240
  • Burgos, Álvarez, M.L., A., Iriondo, N., Sarachaga, I., 2020. Metodología para la transformación de diseños en GRAFCET a código IEC 61131-3. Información Tecnológica, 31(6). https://doi.org/10.4067/S0718-07642020000600133
  • Cimino, Ch., Negri, E., Fumagalli, L., 2019. Review of digital twin applications in manufacturing. Computers in Industry, 113. https://doi.org/10.1016/j.compind.2019.103130
  • Chen, Ch., Fu, H., Zheng,Y., Tao, F., Liu, Y., 2023. The advance of digital twin for predictive maintenance: The role and function of machine learning. Journal of Manufacturing Systems, 71, 581-594. https://doi.org/10.1016/j.jmsy.2023.10.010
  • Cheng, J., Zhang, H., Tao, F., Juang, Ch.F., 2020. DT-II: Digital twin enhanced Industrial Internet reference framework towards smart manufacturing. Robotics and Computer-Integrated Manufacturing, 62, 101881. https://doi.org/10.1016/j.rcim.2019.101881
  • de Prada, C., Galán-Casado, S., Pitarch, J. L., Sarabia, D., Galán, A., Gutiérrez, G., 2022. Gemelos Digitales en la Industria de Procesos. Revista Iberoamericana de Automática e Informática Industrial, 19(3), 285-296. https://doi.org/10.4995/riai.2022.16901
  • Farhadi, A., Lee, S.K.H., Hinchy, E.P., O'Dowd, N.P., McCarthy, C.T., 2022. The development of a digital twin framework for an industrial robotic drilling process. Sensors 22(19), 7232. https://doi.org/10.3390/s22197232
  • Ferko, E., Bucaioni, A., Behnam,M., 2022. Architecting Digital Twins. IEEE Access, 10, 50335-50350. https://doi.org/10.1109/ACCESS.2022.3172964
  • Fuller, A., Fan, Z., Day, C., Barlow, C., 2020. Digital Twin: Enabling Technologies, Challenges and Open Research. IEEE Access, 8, 108952-108971. https://doi.org/10.1109/ACCESS.2020.2998358
  • Ganser, P., Venek, T., Rudel, V., Bergs, T., 2021. DPART - a digital twin framework for the machining domain. MM MM Science Journal, 5134- 5141. https://doi.org/10.17973/MMSJ.2021_11_2021168
  • Hung, M.H., Lin, Y.C., Hsiao, H.C., Chen, C.C., Lai, K.C., Hsieh, Y.M., Tieng, H., Tsai, T.H., Huang, H.C., Yang, H.C., Cheng, F.T., 2022. A novel implementation framework of digital twins for intelligent manufacturing based on container technology and cloud manufacturing services. IEEE Transactions on Automation Science and Engineering, 19(3), 1614-30. https://doi.org/10.1109/TASE.2022.3143832
  • ISO 23247-2, 2021. Automation systems and integration - Digital twin framework for manufacturing - Part 2: Reference architecture. International Organization for Standardization. https://www.iso.org/standard/78743.html
  • ISO/IEC/IEEE 12207, 2017. Systems and software engineering - Software life cycle processes. International Organization for Standardisation. IEC PAS 63088, 2017. Smart manufacturing - Reference architecture model industry 4.0 (RAMI 4.0).
  • IEC 61131-3, 2013. IEC 61131-3, Programmable Controllers, Part 3: Programming Languages.
  • IEC 61512-1, 2002. Batch Control-Part 1: Models and terminology.
  • Iriondo, N., Orive, D., Casquero, O., Marcos, M., 2020. A proposal to introduce digitalization technologies within the automation learning process, IFAC-PapersOnLine, 53(2), 17592-17597. https://doi.org/10.1016/j.ifacol.2020.12.2674
  • Iriondo, N., Álvarez, M.L., Sarachaga, I., Burgos, A., 2022. Unidades de control encapsuladas para sistemas de automatización. XLIII Jornadas de Automática, 7-9 Sep., Logroño, España, 892-899. https://doi.org/10.17979/spudc.9788497498418.0892
  • Jones, D., Snider, Ch., Nassehi, A., Yon, J., Hicks, B., 2020. Characterising the Digital Twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology, 29, 36-52. https://doi.org/10.1016/j.cirpj.2020.02.002
  • Konstantinov, S., Assad, F., Ahmad, B., Vera, D.A., Harrison, R., 2022. Virtual Engineering and Commissioning to Support the Lifecycle of a Manufacturing Assembly System. Machines, 10, 939. https://doi.org/10.3390/machines10100939
  • Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn, W., 2018. Digital Twin in manufacturing: A categorical literature review. IFAC-PapersOnLine, 51(11), 1016-1022. https://doi.org/10.1016/j.ifacol.2018.08.474
  • Liu, S., Bao, J., Zheng, P., 2023. A review of digital twin-driven machining: From digitization to intellectualization. Journal of Manufacturing Systems, 67, 361-378. https://doi.org/10.1016/j.jmsy.2023.02.010
  • Mihai, S., Yaqoob, M., Hung, D.V., Davis, W., Towakel, P., Raza, M., Karamanoglu, M., Barn, B., Shetve, D., Prasad, R.V., Venkataraman, H., Trestian, R., Nguyen, H.X., 2022. Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects. IEEE Communications Surveys & Tutorials, 24(4), 2255-2291. https://doi.org/10.1109/COMST.2022.3208773
  • Montenegro-Bravo, J. S., Ruiz-Flórez, J. D., Romero-Ante, J. D., Manrique-Córdoba, J., Vivas Albán, O. A., Sabater-Navarro, J. M., 2023. Generador 3D de trayectorias libres de colisiones para un manipulador UR3e con pinza blanda. Revista Iberoamericana de Automática e Informática Industrial. https://doi.org/10.4995/riai.2023.19332
  • Moyne, J., Qamsane, Y., Balta, E. C., Kovalenko, I., Faris, J., Barton, K. Tilbury, D. M., 2020. A requirements driven digital twin framework: Specification and opportunities. IEEE Access, 8, 107781-107801. https://doi.org/10.1109/ACCESS.2020.3000437
  • Mylonas, G., Kalogeras, A., Kalogeras, G., Anagnostopoulos, C., Alexakos, C., Muñoz, L., 2021. Digital Twins From Smart Manufacturing to Smart Cities: A Survey. IEEE Access, 9, 143222-143249. https://doi.org/10.1109/ACCESS.2021.3120843
  • Negri, E., Assiro, G., Caioli, L., Fumagalli, L., 2019. A machine state-based digital twin development methodology. In Summer School F. Turco-Industrial Systems Engineering, 34-40.
  • Plakhotnik, D., Curutiu, A., Zhulavskyi, A., Beudaert, X., Munoa, J., Stautner, M., 2021. Framework for coupled digital twins in digital machining. MM Science Journal, 5093-7. https://doi.org/10.17973/MMSJ.2021_11_2021158
  • Shao, G., Helu, M., 2020. Framework for a digital twin in manufacturing: Scope and requirements,'' Manufacturing Letters, 24, 105-107. https://doi.org/10.1016/j.mfglet.2020.04.004
  • Semeraro, C., Lezoche, M., Panetto, H., Dassisti, M., 2021. Digital twin paradigm: A systematic literature review. Computers in Industry, 130, 103469. https://doi.org/10.1016/j.compind.2021.103469
  • Semeraro C, Lezoche M, Panetto H, Dassisti M., 2023a. Data-driven invariant modelling patterns for digital twin design. Journal of Industrial Information Integration, 31, 100424. https://doi.org/10.1016/j.jii.2022.100424
  • Semeraro, C., Aljaghoub, H., Abdelkareem, M. A., Alami, A. H., Dassisti, M., Olabi, A.G., 2023b. Guidelines for designing a digital twin for Li-ion battery: A reference methodology. Energy, 284, 128699. https://doi.org/10.1016/j.energy.2023.128699
  • Orive, D., Iriondo, N., Burgos, A., Sarachaga, I., Alvarez, M.L., Marcos, M., 2019. Fault injection in Digital Twins as a means to test the response to process faults at virtual commissioning. 24th IEEE Conference on Emerging Technologies and Factory Automation (ETFA), 1230-1234. https://doi.org/10.1109/ETFA.2019.8869334
  • Prados, C., Hernando, M., Gambao, E., Brunete, A., 2022. ROMERIN: Organismo robótico escalador basado en patas modulares con ventosas activas. Revista Iberoamericana de Automática e Informática Industrial, 20(2), 175-186. https://doi.org/10.4995/riai.2022.18749
  • Qamsane, Y., Moyne, J., Toothman, M., Kovalenko, I., Balta, E.C., Faris, J., Tilbury, D.M., Barton, K., 2021. A Methodology to Develop and Implement Digital Twin Solutions for Manufacturing Systems. IEEE Access, 9, 44247-44265. https://doi.org/10.1109/ACCESS.2021.3065971
  • Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y.,Wang, L., Nee, A.Y.C., 2021. Enabling technologies and tools for digital twin. Journal of Manufacturing Systems, 58, 3-21. https://doi.org/10.1016/j.jmsy.2019.10.001
  • Rasheed, A., San, O., Kvamsdal, T., 2020. Digital Twin: Values, Challenges and Enablers From a Modeling Perspective. IEEE Access, 8, 21980-22012. https://doi.org/10.1109/ACCESS.2020.2970143
  • Schroeder, G.N., Steinmetz, C., Rodrigues, R.N., Henriques, R.V.B., Rettberg, A., Pereira, C.E., 2021. A Methodology for Digital Twin Modeling and Deployment for Industry 4.0. Proceedings of the IEEE, 109(4), 556-567. https://doi.org/10.1109/JPROC.2020.3032444
  • Shao, G., Frechette, S., Srinivasan, V., 2023. An Analysis of the New ISO 23247 Series of Standards on Digital Twin Framework for Manufacturing. Manufacturing Science and Engineering Conference, 12-16 Junio, New Brunswick, USA. https://doi.org/10.1115/MSEC2023-101127
  • Tao, F., Zhang, H., Liu, A., Nee, A. Y. C., 2019. Digital Twin in Industry: State-of-the-Art. IEEE Transactions on Industrial Informatics, 15(4), 2405- 2415. https://doi.org/10.1109/TII.2018.2873186
  • Wang, P., Luo, M., 2021. A digital twin-based big data virtual and real fusion learning reference framework supported by industrial internet towards smart manufacturing. Journal of Manufacturing Systems, 58, 16-32. https://doi.org/10.1016/j.jmsy.2020.11.012
  • Zhang, Y. F., Shao, Y. Q., Wang, J. F., Li, S. Q., 2020. Digital Twin-based Production Simulation of Discrete Manufacturing Shop-floor for Onsite Performance Analysis. IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore, 1107- 1111. https://doi.org/10.1109/IEEM45057.2020.9309928
  • Zhou, X., Xu, X., Liang, W., Zeng, Z., Shimizu, S., Yang, L.T., Jin, Q., 2022. Intelligent Small Object Detection for Digital Twin in Smart Manufacturing with Industrial Cyber-Physical Systems. IEEE Transactions on Industrial Informatics, 18(2), 1377-1386. https://doi.org/10.1109/TII.2021.3061419
Fuente de los datos: Dialnet