Gemelos Digitales en la Industria 5.0 – una Revisión Sistemática de Literatura

Autores/as

DOI:

https://doi.org/10.31637/epsir-2024-641

Palabras clave:

Gemelos Digitales, Industria 5.0, Colaboración Humano-Robot, Seguridad de los Trabajadores, Eficiencia en la Fabricación, Inteligencia Artificial, Realidad Aumentada, Sistemas Centrados en el Ser Humano

Resumen

Introducción: La Industria 5.0 integra tecnologías avanzadas con enfoques centrados en el ser humano para mejorar la seguridad en la fabricación, la colaboración humano-robot y la eficiencia. Los gemelos digitales, réplicas virtuales de sistemas físicos, son centrales en esta iniciativa para mejorar la seguridad laboral y la eficiencia operativa. Metodología: Esta SLR utilizó una estrategia de búsqueda exhaustiva en cinco bibliotecas digitales: IEEE Explore, Scopus, Taylor & Francis Online, ACM Digital Library y Web of Science. Resultados: Los hallazgos destacan las contribuciones de los gemelos digitales a la seguridad de los trabajadores mediante el monitoreo en tiempo real, la detección inteligente y la gestión proactiva de riesgos. La colaboración humano-robot se logra a través de la integración de datos en tiempo real. Los gemelos digitales también mejoran la eficiencia en la fabricación al permitir sistemas de producción más inteligentes y adaptativos. Discusión: A pesar de su potencial, se deben abordar desafíos como la calidad de los datos, la complejidad computacional, la ciberseguridad, los factores humanos y los impactos socioeconómicos. Conclusiones: Esta SLR subraya el papel de los gemelos digitales en el avance de la Industria 5.0, promoviendo entornos industriales más seguros, eficientes y centrados en el ser humano.

Descargas

Los datos de descargas todavía no están disponibles.

Biografía del autor/a

Lauren Genith Isaza Domínguez, Corporación Universitaria Minuto de Dios

Ingeniería Electromecánica, Magister en Gestión de la Tecnología Educativa y Doctora en Sostenibilidad. Docente en instituciones de educación superior con 13 años de experiencia, experiencia laboral en el área de mantenimiento e interventoría de obras eléctricas. Investigadora Junior reconocida ante el ministerio de ciencia y tecnología, experiencia en grupos de investigación, como líder de semilleros de investigación, asesora de proyectos de investigación y experiencia en proyectos académicos.

Citas

Alimam, H., Mazzuto, G., Tozzi, N., Ciarapica, F. E., & Bevilacqua, M. (2023). The resurrection of digital triplet: A cognitive pillar of human-machine integration at the dawn of industry 5.0. Journal of King Saud University - Computer and Information Sciences, 35(10), 101846. https://doi.org/10.1016/j.jksuci.2023.101846 DOI: https://doi.org/10.1016/j.jksuci.2023.101846

Asad, U., Khan, M., Khalid, A., & Lughmani, W. A. (2023). Human-centric digital twins in industry: A comprehensive review of enabling technologies and implementation strategies. Sensors, 23(8), 3938. https://doi.org/10.3390/s23083938 DOI: https://doi.org/10.3390/s23083938

Ávila-Gutiérrez, M. J., Suarez-Fernandez de Miranda, S., & Aguayo-González, F. (2022). Occupational safety and health 5.0—A model for multilevel strategic deployment aligned with the sustainable development goals of agenda 2030. Sustainability, 14(11), 6741. https://doi.org/10.3390/su14116741 DOI: https://doi.org/10.3390/su14116741

Baniqued, P. D. E., Bremner, P., Sandison, M., Harper, S., Agrawal, S., Bolarinwa, J., Blanche, J. Jiang, Z., Johnson, T., Mitchell, D., Lopez Pulgarin, E. J., West, A., Willis, M., Yao, K., Flynn, D., Giuliani, M., Groves, K., Lennox, B., & Watson, S. (2024). Multimodal immersive digital twin platform for cyber–physical robot fleets in nuclear environments. Journal of Field Robotics, 41(5), 1521-1540. https://doi.org/10.1002/rob.22329 DOI: https://doi.org/10.1002/rob.22329

Berti, N., & Finco, S. (2022). Digital twin and human factors in manufacturing and logistics systems: State of the art and future research directions. IFAC-PapersOnLine, 55(10), 1893-1898. https://doi.org/10.1016/j.ifacol.2022.09.675 DOI: https://doi.org/10.1016/j.ifacol.2022.09.675

Berti, N., Finco, S., Guidolin, M., & Battini, D. (2023). Towards human digital twins to enhance workers' safety and production system resilience. IFAC-PapersOnLine, 56(2), 11062-11067. https://doi.org/10.1016/j.ifacol.2023.10.809 DOI: https://doi.org/10.1016/j.ifacol.2023.10.809

Bhattacharya, M., Penica, M., O’Connell, E., Southern, M., & Hayes, M. (2023). Human-in loop: a review of smart manufacturing deployments. Systems, 11(1), 35. https://doi.org/10.3390/systems11010035 DOI: https://doi.org/10.3390/systems11010035

Cimino, A., Elbasheer, M., Longo, F., Nicoletti, L., & Padovano, A. (2023). Empowering field operators in manufacturing: a prospective towards industry 5.0. Procedia Computer Science, 217, 1948-1953. https://doi.org/10.1016/j.procs.2022.12.395 DOI: https://doi.org/10.1016/j.procs.2022.12.395

Constantinescu, C., Rus, R., Rusu, C. A., & Popescu, D. (2019). Digital twins of exoskeleton centered workplaces: Challenges and development methodology. Procedia Manufacturing, 39, 58-65. https://doi.org/10.1016/j.promfg.2020.01.228 DOI: https://doi.org/10.1016/j.promfg.2020.01.228

Coronado, E., Ueshiba, T., & Ramirez-Alpizar, I. G. (2024). A path to Industry 5.0 digital twins for human–robot collaboration by bridging NEP+ and ROS. Robotics, 13(2), 28. https://doi.org/10.3390/robotics13020028 DOI: https://doi.org/10.3390/robotics13020028

David, J., Lobov, A., & Lanz, M. (2018, October). Learning experiences involving digital twins. In IECON 2018-44th annual conference of the IEEE industrial electronics Society (pp. 3681-3686). IEEE. https://doi.org/10.1109/IECON.2018.8591460 DOI: https://doi.org/10.1109/IECON.2018.8591460

Davila-Gonzalez, S., & Martin, S. (2024). Human digital twin in Industry 5.0: A holistic approach to worker safety and well-being through advanced AI and emotional analytics. Sensors, 24(2), 655. https://doi.org/10.3390/s24020655 DOI: https://doi.org/10.3390/s24020655

El-Agamy, R. F., Sayed, H. A., AL Akhatatneh, A. M., Aljohani, M., & Elhosseini, M. (2024). Comprehensive analysis of digital twins in smart cities: a 4200-paper bibliometric study. Artificial Intelligence Review, 57(6), 154. https://doi.org/10.1007/s10462-024-10781-8 DOI: https://doi.org/10.1007/s10462-024-10781-8

Feddoul, Y., Ragot, N., Duval, F., Havard, V., Baudry, D., & Assila, A. (2023). Exploring human-machine collaboration in industry: A systematic literature review of digital twin and robotics interfaced with extended reality technologies. The International Journal of Advanced Manufacturing Technology, 129(5), 1917-1932. https://doi.org/10.1007/s00170-023-12291-3 DOI: https://doi.org/10.1007/s00170-023-12291-3

Fernández, M. M., Delrieux, C., & Muñoz, J. Á. F. (2022, July). Automated personnel digital twinning in industrial workplaces. In 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET) (pp. 1-6). IEEE. https://doi.org/10.1109/ICECET55527.2022.9872882 DOI: https://doi.org/10.1109/ICECET55527.2022.9872882

Franciosi, C., Miranda, S., Veneroso, C. R., & Riemma, S. (2023). Investigating human factors integration into DT-based joint production and maintenance scheduling. In Alfnes, E., Romsdal, A., Strandhagen, J.O., von Cieminski, G., Romero, D. (Eds.), Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures (Vol. 689, pp. 633-648). Springer. https://doi.org/10.1007/978-3-031-43662-8_45 DOI: https://doi.org/10.1007/978-3-031-43662-8_45

Grimmeisen, P., Golwalkar, R., Ma, Y., & Morozov, A. (2023). Automated and continuous risk assessment for ROS-based software-defined robotic systems. In 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE) (pp. 1-7). IEEE. https://doi.org/10.1109/CASE56687.2023.10260416 DOI: https://doi.org/10.1109/CASE56687.2023.10260416

He, Q., Li, L., Li, D., Peng, T., Zhang, X., Cai, Y., Cai, Y., Zhang, X., & Tang, R. (2024). From digital human modeling to human digital twin: Framework and perspectives in human factors. Chinese Journal of Mechanical Engineering, 37(9). https://doi.org/10.1186/s10033-024-00998-7 DOI: https://doi.org/10.1186/s10033-024-00998-7

Jimenez, J. F., & Maire, J. L. (2023, September). ErgoTwin: A digital twin model for monitoring the postural risks on industrial workers. In Borangiu, T., Trentesaux, D., Leitão, P., Berrah, L., Jimenez, JF. (Eds.), Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future. SOHOMA 2023. Studies in Computational Intelligence (Vol. 1136, pp. 250-262). Springer. https://doi.org/10.1007/978-3-031-53445-4_21 DOI: https://doi.org/10.1007/978-3-031-53445-4_21

Kamdjou, H. M., Baudry, D., Havard, V., & Ouchani, S. (2024). Resource-constrained extended reality operated with digital twin in industrial Internet of Things. In IEEE Open Journal of the Communications Society (Vol. 5, pp. 928-950). https://doi.org/10.1109/OJCOMS.2024.3356508 DOI: https://doi.org/10.1109/OJCOMS.2024.3356508

Khosravy, M., Gupta, N., Pasquali, A., Dey, N., Crespo, R. G., & Witkowski, O. (2023). Human-collaborative artificial intelligence along with social values in Industry 5.0: A survey of the state-of-the-art. IEEE Transactions on Cognitive and Developmental Systems, 16(1), 165-176. https://doi.org/10.1109/TCDS.2023.3326192 DOI: https://doi.org/10.1109/TCDS.2023.3326192

Kolesnikov, M. V., Atmojo, U. D., & Vyatkin, V. (2023, October). Data-driven human factors enabled digital twin. In IECON 2023-49th Annual Conference of the IEEE Industrial Electronics Society (pp. 1-6). IEEE. https://doi.org/10.1109/IECON51785.2023.10311802 DOI: https://doi.org/10.1109/IECON51785.2023.10311802

Kovič, K., Javernik, A., Ojsteršek, R., & Palčič, I. (2024). The impact of changing collaborative workplace parameters on assembly operation efficiency. Robotics, 13(3), 36. https://doi.org/10.3390/robotics13030036 DOI: https://doi.org/10.3390/robotics13030036

Krupas, M., Kajati, E., Liu, C., & Zolotova, I. (2024). Towards a human-centric digital twin for human–machine collaboration: A review on enabling technologies and methods. Sensors, 24(7), 2232. https://doi.org/10.3390/s24072232 DOI: https://doi.org/10.3390/s24072232

Lago Alvarez, A., Mohammed, W. M., Vu, T., Ahmadi, S., & Martinez Lastra, J. L. (2023). Enhancing digital twins of semi-automatic production lines by digitizing operator skills. Applied Sciences, 13(3), 1637. https://doi.org/10.3390/app13031637 DOI: https://doi.org/10.3390/app13031637

Leng, J., Zhu, X., Huang, Z., Xu, K., Liu, Z., Liu, Q., & Chen, X. (2023). ManuChain II: Blockchained smart contract system as the digital twin of decentralized autonomous manufacturing toward resilience in industry 5.0. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(8), 4715-4728. https://doi.org/10.1109/TSMC.2023.3257172 DOI: https://doi.org/10.1109/TSMC.2023.3257172

Lewis, J., Schneegans, S., & Straza, T. (Eds.) (2021). UNESCO Science Report: The race against time for smarter development (Vol. 2021). Unesco Publishing. https://acortar.link/qOpnEw

Li, C., Zheng, P., Li, S., Pang, Y., & Lee, C. K. (2022). AR-assisted digital twin-enabled robot collaborative manufacturing system with human-in-the-loop. Robotics and Computer Integrated Manufacturing, 76, 102321. https://doi.org/10.1016/j.rcim.2022.102321 DOI: https://doi.org/10.1016/j.rcim.2022.102321

Longo, F., Padovano, A., De Felice, F., Petrillo, A., & Elbasheer, M. (2023). From “prepare for the unknown” to “train for what's coming”: a digital twin-driven and cognitive training approach for the workforce of the future in smart factories. Journal of Industrial Information Integration, 32, 100437. https://doi.org/10.1016/j.jii.2023.100437 DOI: https://doi.org/10.1016/j.jii.2023.100437

Luxenburger, A., Mohr, J., Merkel, D., Knoch, S., Porta, D., Paul, C., Widenka, J., Schäfers, P., Baumann, M., Lehnhoff, S., & Schwab, J. (2024, January). Interactive digital twins for online planning and worker safety in intralogistics and production. In 2024 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR) (pp. 66-74). IEEE. https://doi.org/10.1109/AIxVR59861.2024.00016 DOI: https://doi.org/10.1109/AIxVR59861.2024.00016

Maruyama, T., Ueshiba, T., Tada, M., Toda, H., Endo, Y., Domae, Y., Nakabo, Y., Mori, T., & Suita, K. (2021). Digital twin-driven human robot collaboration using a digital human. Sensors, 21(24), 8266. https://doi.org/10.3390/s21248266 DOI: https://doi.org/10.3390/s21248266

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 DOI: https://doi.org/10.1109/COMST.2022.3208773

Mincă, E., Filipescu, A., Cernega, D., Șolea, R., Filipescu, A., Ionescu, D., & Simion, G. (2022). Digital twin for a multifunctional technology of flexible assembly on a mechatronics line with integrated robotic systems and mobile visual sensor—Challenges towards Industry 5.0. Sensors, 22(21), 8153. https://doi.org/10.3390/s22218153 DOI: https://doi.org/10.3390/s22218153

Mourad, N., Alsattar, H. A., Qahtan, S., Zaidan, A. A., Deveci, M., Sangaiah, A. K., & Pedrycz, W. (2023). Optimising control engineering tools using digital twin capabilities and other cyber-physical metaverse manufacturing system components. IEEE Transactions on Consumer Electronics, 70(1), 3212-3221. https://doi.org/10.1109/TCE.2023.3326047 DOI: https://doi.org/10.1109/TCE.2023.3326047

Müller, M., Ruppert, T., Jazdi, N., & Weyrich, M. (2023). Self-improving situation awareness for human–robot-collaboration using intelligent digital twin. Journal of Intelligent Manufacturing, 35, 2045-2063. https://doi.org/10.1007/s10845-023-02138-9 DOI: https://doi.org/10.1007/s10845-023-02138-9

Ouahabi, N., Chebak, A., Kamach, O., Laayati, O., & Zegrari, M. (2024). Leveraging digital twin into dynamic production scheduling: A review. Robotics and Computer-Integrated Manufacturing, 89, 102778. https://doi.org/10.1016/j.rcim.2024.102778 DOI: https://doi.org/10.1016/j.rcim.2024.102778

Peter, O. A., Anastasia, S. D., & Muzalevskii, A. R. (2021, June). The implementation of Smart factory for product inspection and validation A step by step guide to the implementation of the virtual plant of a smart factory using digital twin. In 2021 10th Mediterranean Conference on Embedded Computing (MECO) (pp. 1-7). IEEE. https://doi.org/10.1109/MECO52532.2021.9460140 DOI: https://doi.org/10.1109/MECO52532.2021.9460140

Piccarozzi, M., Silvestri, L., Silvestri, C., & Ruggieri, A. (2024). Roadmap to Industry 5.0: Enabling technologies, challenges, and opportunities towards a holistic definition in management studies. Technological Forecasting and Social Change, 205, 123467. https://doi.org/10.1016/j.techfore.2024.123467 DOI: https://doi.org/10.1016/j.techfore.2024.123467

Proia, S., Carli, R., Cavone, G., & Dotoli, M. (2021). Control techniques for safe, ergonomic, and efficient human-robot collaboration in the digital industry: A survey. IEEE Transactions on Automation Science and Engineering, 19(3), 1798-1819. https://doi.org/10.1109/TASE.2021.3131011 DOI: https://doi.org/10.1109/TASE.2021.3131011

Qu, Y., Zhao, N., & Zhang, H. (2024). Digital twin technology of human–machine integration in cross-belt sorting system. Chinese Journal of Mechanical Engineering, 37(33). https://doi.org/10.1186/s10033-024-01012-w DOI: https://doi.org/10.1186/s10033-024-01012-w

Raffik, R., Sathya, R. R., Vaishali, V., & Balavedhaa, S. (2023). Industry 5.0: Enhancing human-robot collaboration through collaborative robots–A review. In 2023 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA) (pp. 1-6). IEEE. https://doi.org/10.1109/ICAECA56562.2023.10201120 DOI: https://doi.org/10.1109/ICAECA56562.2023.10201120

Roy, S., & Singh, S. (2024). XR and digital twins, and their role in human factor studies. Frontiers in Energy Research, 12, 1359688. https://doi.org/10.3389/fenrg.2024.1359688 DOI: https://doi.org/10.3389/fenrg.2024.1359688

Rožanec, J. M., Novalija, I., Zajec, P., Kenda, K., Tavakoli Ghinani, H., Suh, S., Veliou, E., Papamartzivanos, D., Giannetsos, T., Menesidou, S. A., Alonso, R., Cauli, N. Meloni, A., Reforgiato Recupero, D., Kyriazis, D., Sofianidis, G., Theodoropoulos, S., Fortuna, B., Mladenić, D., & Soldatos, J. (2023). Human-centric artificial intelligence architecture for industry 5.0 applications. International Journal of Production Research, 61(20), 6847-6872. https://doi.org/10.1080/00207543.2022.2138611 DOI: https://doi.org/10.1080/00207543.2022.2138611

Tallat, R., Hawbani, A., Wang, X., Al-Dubai, A., Zhao, L., Liu, Z., Min, G., Zomaya, A. Y., & Alsamhi, S. H. (2023). Navigating industry 5.0: A survey of key enabling technologies, trends, challenges, and opportunities. IEEE Communications Surveys & Tutorials, 26(2), 1080-1126. https://doi.org/10.1109/COMST.2023.3329472 DOI: https://doi.org/10.1109/COMST.2023.3329472

Tosoni, F., Dall'Ora, N., Fraccaroli, E., & Fummi, F. (2022). The challenges of coupling digital-twins with multiple classes of faults. In 2022 IEEE 23rd Latin American Test Symposium (LATS) (pp. 1-6). IEEE. https://doi.org/10.1109/LATS57337.2022.9937026 DOI: https://doi.org/10.1109/LATS57337.2022.9937026

Tóth, A., Nagy, L., Kennedy, R., Bohuš, B., Abonyi, J., & Ruppert, T. (2023). The human centric industry 5.0 collaboration architecture. MethodsX, 11, 102260. https://doi.org/10.1016/j.mex.2023.102260 DOI: https://doi.org/10.1016/j.mex.2023.102260

Ungureanu, A. V. (2020, August). The transition from industry 4.0 to industry 5.0. The 4Cs of the global economic change. In 16th Economic International Conference NCOE 4.0 2020 (Vol. 13, pp. 70-81). Editura Lumen, Asociatia Lumen. https://www.proceedings.lumenpublishing.com/ojs/index.php/lumenproceedings/article/download/319/342 DOI: https://doi.org/10.18662/lumproc/ncoe4.0.2020/07

Vilar-Dias, J. L., Junior, A. S. S., & Lima-Neto, F. B. (2023). An interpretable digital twin for self-aware industrial machines. Sensors, 24(1), 4. https://doi.org/10.3390/s24010004 DOI: https://doi.org/10.3390/s24010004

Wang, B., Zhou, H., Li, X., Yang, G., Zheng, P., Song, C., & Wang, L. (2024). Human digital twin in the context of Industry 5.0. Robotics and Computer-Integrated Manufacturing, 85, 102626. https://doi.org/10.1016/j.rcim.2023.102626 DOI: https://doi.org/10.1016/j.rcim.2023.102626

Wang, H., Lv, L., Li, X., Li, H., Leng, J., Zhang, Y., Thomson, V., Liu, G., Wen, X., Sun, C., & Luo, G. (2023). A safety management approach for Industry 5.0′s human-centered manufacturing based on digital twin. Journal of Manufacturing Systems, 66, 1-12. https://doi.org/10.1016/j.jmsy.2022.11.013 DOI: https://doi.org/10.1016/j.jmsy.2022.11.013

Wang, S., Zhang, J., Wang, P., Law, J., Calinescu, R., & Mihaylova, L. (2024). A deep learning enhanced digital twin framework for improving safety and reliability in human robot collaborative manufacturing. Robotics and Computer-Integrated Manufacturing, 85, 102608. https://doi.org/10.1016/j.rcim.2023.102608 DOI: https://doi.org/10.1016/j.rcim.2023.102608

Xiang, W., Yu, K., Han, F., Fang, L., He, D., & Han, Q. L. (2023). Advanced manufacturing in industry 5.0: A survey of key enabling technologies and future trends. IEEE Transactions on Industrial Informatics, 20(2), 1055-1068. https://doi.org/10.1109/TII.2023.3274224 DOI: https://doi.org/10.1109/TII.2023.3274224

Xiao, L., Han, D., Yang, C., Cai, J., Liang, W., & Li, K. C. (2023). TS-DP: An efficient data processing algorithm for distribution digital twin grid for Industry 5.0. IEEE Transactions on Consumer Electronics, 70(1), 1983-1994. https://doi.org/10.1109/TCE.2023.3332099 DOI: https://doi.org/10.1109/TCE.2023.3332099

Xie, J., Liu, Y., Wang, X., Fang, S., & Liu, S. (2024). A new XR-based human‐robot collaboration assembly system based on industrial metaverse. Journal of Manufacturing Systems, 74, 949-964. https://doi.org/10.1016/j.jmsy.2024.05.001 DOI: https://doi.org/10.1016/j.jmsy.2024.05.001

Xu, X., Lu, Y., Vogel-Heuser, B., & Wang, L. (2021). Industry 4.0 and Industry 5.0—Inception, conception and perception. Journal of manufacturing systems, 61, 530-535. https://doi.org/10.1016/j.jmsy.2021.10.006 DOI: https://doi.org/10.1016/j.jmsy.2021.10.006

Yin, Y., Zheng, P., Li, C., & Wang, L. (2023). A state-of-the-art survey on augmented reality assisted digital twin for futuristic human-centric industry transformation. Robotics and Computer-Integrated Manufacturing, 81, 102515. https://doi.org/10.1016/j.rcim.2022.102515 DOI: https://doi.org/10.1016/j.rcim.2022.102515

Zhang, Q., Wei, Y., Liu, Z., Duan, J., & Qin, J. (2023). A framework for service-oriented digital twin systems for discrete workshops and its practical case study. Systems, 11(3), 156. https://doi.org/10.3390/systems11030156 DOI: https://doi.org/10.3390/systems11030156

Descargas

Publicado

2024-08-30

Cómo citar

Isaza Domínguez, L. G. (2024). Gemelos Digitales en la Industria 5.0 – una Revisión Sistemática de Literatura. European Public & Social Innovation Review, 9, 1–21. https://doi.org/10.31637/epsir-2024-641

Número

Sección

Investigación e Inteligencia Artificial