Evaluación del uso de gemelos digitales en los sistemas de producción
DOI:
https://doi.org/10.15649/2346030X.4382Palabras clave:
gemelos digitales, industria 5.0, optimización de producción, análisis predictivo, mejora continuaResumen
La presente investigación tuvo como objetivo evaluar el uso de gemelos digitales empleados en los sistemas de producción, como herramienta de mejoramiento de los procesos para la adaptación organizacional enfocado a la industria 5.0. Utilizando la metodología PRISMA, se llevó a cabo una revisión sistemática de literatura publicada en los últimos cinco años en las bases de datos de Scielo, Science Direct, Redalyc y Scopus, centrada en identificar, analizar y evaluar los modelos de gemelos digitales aplicados en diferentes contextos de producción. Los resultados indican una variedad de modelos de gemelos digitales, desde sistemas integrados de simulación y análisis de datos hasta soluciones específicas para optimización de procesos y mantenimiento preventivo. Se observó que los gemelos digitales mejoran significativamente la eficiencia operativa, la gestión de inventarios y la capacidad de adaptación en respuesta a las fluctuaciones del mercado. Además, facilitan la integración de nuevas tecnologías y promueven una cultura de mejora continua y adaptabilidad dentro de las organizaciones. En conclusión, los gemelos digitales se presentan como herramientas cruciales para el avance hacia la industria 5.0, ofreciendo beneficios significativos en términos de eficiencia y adaptabilidad operacional. Su implementación permite a las empresas mejorar la toma de decisiones, aumentar la eficiencia operativa, reducir costos, optimizar procesos, además de responder más rápido y efectivo a las demandas del entorno competitivo actual y cambiante.
Referencias
E. Ferko, A. Bucaioni, and M. Behnam, “Architecting Digital Twins,” IEEE Access, vol. PP(1), 2022.
J. A. P. Rodriguez, C. G. R. Borges, A. V Pérez, and C. A. Bowen, “Emulation of System as Strategy for Teaching of Mechanical System,” International Journal of Psychosocial Rehabilitation, vol. 24, no. 2, 2020.
A. Filipescu et al., “Digital Twin for a Mechatronics Line with Integrated Mobile Robotic Systems,” in 2022 26th International Conference on System Theory, Control and Computing (ICSTCC), 2022.
L. Zhao, Z. Nie, Y. Xia, and H. Li, “Virtual–Physical Tracking Control for a Car-Like Mobile Robot Based on Digital Twin Technology,” IEEE Transactions on Industrial Electronics, 2024, doi: 10.1109/TIE.2024.3384585.
S. Sai, M. Prasad, A. Garg, and V. Chamola, “Synergizing Digital Twins and Metaverse for Consumer Health: A Case Study Approach,” IEEE Transactions on Consumer Electronics, 2024, doi: 10.1109/TCE.2024.3367929.
P. M. Ariza and A. J. Vallecillo-Moreno, “¿ Qué es un Gemelo Digital? Una Propuesta de Arquitectura para su Implementación,” 2023.
M. S. Amjad, M. Z. Rafique, and M. A. Khan, “Leveraging optimized and cleaner production through industry 4.0,” Sustainable Production and Consumption, vol. 26, pp. 859–871, 2021.
D. A. Sosfenov, “DIGITAL TWIN: HISTORY OF ORIGIN AND DEVELOPMENT PROSPECTS,” Intellect. Innovations. Investments, 2023, doi: 10.25198/2077-7175-2023-4-35.
S. de López Diz, R. M. López, F. J. R. Sánchez, E. D. Llerena, and E. J. B. Peña, “A real-time digital twin approach on three-phase power converters applied to condition monitoring,” Applied Energy, vol. 334, p. 120606, 2023.
M. Kim and S. Kim, “Development of a dedicated process simulator for the digital twin in apparel manufacturing: a case study,” International Journal of Clothing Science and Technology, vol. 36, no. 4, pp. 629–645, 2024.
B. Moya, A. Badias, I. Alfaro, F. Chinesta, and E. Cueto, “Digital twins that learn and correct themselves,” International Journal for Numerical Methods in Engineering, vol. 123, no. 12, pp. 3034–3044, 2020, doi: 10.1002/nme.6535.
Z. Lv, “Gemelos digitales en la Industria 5.0,” Research, vol. 6, 2023.
Z. Cinar, A. A. Nuhu, Q. Zeeshan, and O. Korhan, “Digital Twins for Industry 4.0: A Review,” 2019.
A. A. Adamou and C. Alaoui, “Energy efficiency model-based digital shadow for induction motors: towards the implementation of a digital twin,” Engineering Science and Technology, an International Journal, vol. 44, p. 101469, 2023.
M. Trstenjak and P. Cosic, “Process planning in Industry 4.0 environment,” Procedia Manufacturing, vol. 11, pp. 1744–1750, 2017, doi: 10.1016/j.promfg.2017.07.303.
D. Yan et al., “Digital twin and parameter correlation-enabled variant design of production lines,” International Journal of Computer Integrated Manufacturing, pp. 1–22, 2023, doi: 10.1080/0951192X.2023.2294447.
R. van Dinter, B. Tekinerdogan, and C. Catal, “Reference architecture for digital twin-based predictive maintenance systems,” Computers & Industrial Engineering, vol. 177, p. 109099, 2023, doi: 10.1016/j.cie.2023.109099.
B. Chen, J. Wan, L. Shu, P. Li, M. Mukherjee, and B. Yin, “Smart factory of industry 4.0: Key technologies, application case, and challenges,” IEEE Access, vol. 6, pp. 6505–6519, 2017.
G. E. Modoni and M. Sacco, “Un marco basado en gemelos digitales humanos impulsando la centricidad humana hacia la Industria 5.0,” Sensores (Basilea, Suiza), vol. 23, 2023.
S. Jagatheesaperumal et al., “Architecting Digital Twins,” IEEE Access, vol. 24, no. 1, p. 7539, 2023.
C. Cimino, E. Negri, and L. Fumagalli, “Review of digital twin applications in manufacturing,” Computers in Industry, vol. 113, 2019.
Q. Qi and F. Tao, “Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison,” Ieee Access, vol. 6, pp. 3585–3593, 2018, doi: 10.1109/ACCESS.2018.2793265.
L. F. C. S. Durão, S. Haag, R. Anderl, K. Schützer, and E. Zancul, “Digital Twin Requirements in the Context of Industry 4.0,” 2018.
A. Padovano, F. Longo, L. Manca, and R. Grugni, “Improving safety management in railway stations through a simulation-based digital twin approach,” Computers & Industrial Engineering, vol. 187, p. 109839, 2024, doi: 10.1016/j.cie.2023.109839.
H. D. Perez, J. M. Wassick, and I. E. Grossmann, “A digital twin framework for online optimization of supply chain business processes,” Computers & Chemical Engineering, vol. 166, p. 107972, 2022, doi: 10.1016/j.compchemeng.2022.107972.
A. Fuller, Z. Fan, and C. Day, “Digital Twin: Enabling Technologies, Challenges and Open Research,” IEEE Access, vol. 8, pp. 108952–108971, 2019.
D. Opoku, S. Perera, R. Osei-Kyei, M. Rashidi, K. Bamdad, and T. Famakinwa, “Barriers to the Adoption of Digital Twin in the Construction Industry: A Literature Review,” Informatics, 2023, doi: 10.3390/informatics10010014.
P. D. Winter and T. Chico, “Using the Non-Adoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) Framework to Identify Barriers and Facilitators for the Implementation of Digital Twins in Cardiovascular Medicine,” Sensors (Basel, Switzerland), vol. 23, no. 14, 2023, doi: 10.3390/s23146333.
R. Ojsteršek, A. Javernik, and B. Buchmeister, “Optimizing smart manufacturing systems using digital twin,” Advances in Production Engineering & Management, 2023, doi: 10.14743/apem2023.4.486.
S. Abdoli, “Experimentable Digital Twin for Virtual Validation of Manufacturing Systems,” in Proceedings of the 2023 10th International Conference on Industrial Engineering and Applications, 2023.
D. Lee et al., “Digital Twin-Based Analysis and Optimization for Design and Planning of Production Lines,” Machines, vol. 10, no. 12, p. 1147, 2022.
Q. Charrier et al., “Towards the Augmentation of Digital Twin Performance,” Sensors (Basel, Switzerland), 2023.
Y. Dong, Q. Chen, W. Ding, N. Shao, G. Chen, and G. Li, “State Evaluation and Fault Prediction of Protection System Equipment Based on Digital Twin Technology,” Applied Sciences, vol. 12(15), p. 7539, 2022.
N. C. Schäfer, P. Burggräf, and T. Adlon, “Application of a Digital Twin for Proactive Production Planning,” in Day 2 Wed, September 28, 2022, 2022. doi: 10.5957/smc-2022-058.
A. Zakharchenko and O. Stepanets, “Orchestration of model computing assets for the development of digital twins,” Modeling Control and Information Technologies, 2023, doi: 10.31713/mcit.2023.072.
L. Magalhães et al., “Conceiving a Digital Twin for a Flexible Manufacturing System,” Applied Sciences, vol. 12, no. 19, p. 9864, 2022, doi: 10.3390/app12199864.
Q., Xie, E.J., Schenck, H. S., Yang, Y., Chen, Y., Peng and F. Wang, "Faithful AI in Medicine: A Systematic Review with Large Language Models and Beyond," Research Square, vol. 8, no. 1, pp. 1-12, 2023. DOI: 10.21203/rs.3.rs-3661764/v1.
E., Hechler, M., Weihrauch and Y. Wu, "Data Fabric and Data Mesh Approaches with AI," Apress, vol. 4, pp. 1-19, 2023. DOI: 10.1007/978-1-4842-9253-2_8.
Y., Kang, H., Du, A., Forkan, P., Jayaraman, A., Aryani and T. Sellis, "ExpFinder: A Hybrid Model for Expert Finding From Text-based Expertise Data," Expert Systems with Applications, vol. 211, pp. 118691, 2023. DOI: 10.1016/j.eswa.2022.118691.
X. Li, J. Du, X. Wang, D. Yang, and B. Yang, “Research on Digital Twin Technology for Production Line Design and Simulation,” in ICMSO 2019, 2019, pp. 516–522. doi: 10.1007/978-3-030-34387-3_64.
K. Židek, J. Pitel’, M. Adamek, P. Lazorík, and A. Hošovský, “Digital Twin of Experimental Smart Manufacturing Assembly System for Industry 4.0 Concept,” Sustainability, 2020, doi: 10.3390/su12093658.
V. Makarov, A. Bakhtizin, and G. Beklaryan, “Developing digital twins for production enterprises,” Business Informatics, vol. 13, no. 4, pp. 7–16, 2019, doi: 10.17323/1998-0663.2019.4.7.16.
A. Ait-Alla, M. Kreutz, D. Rippel, M. Lütjen, and M. Freitag, “Simulation-based Analysis of the Interaction of a Physical and a Digital Twin in a Cyber-Physical Production System,” in IFAC-PapersOnLine, 2019.
M. Glatt, C. Sinnwell, L. Yi, S. Donohoe, B. Ravani, and J. Aurich, “Modeling and implementation of a digital twin of material flows based on physics simulation,” Journal of Manufacturing Systems, 2020.
S. Jeon and S. Schuesslbauer, “Digital Twin Application for Production Optimization,” in 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2020, pp. 542–545.
B. Wang, L. Yuan, X. Yu, and L. Ou, “Construction and Optimization of Digital Twin Model for Hardware Production Line,” in IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, 2020, pp. 4756–4761. doi: 10.1109/IECON43393.2020.9254967.
Z. Huang, Y. Shen, J. Li, M. Fey, and C. Brecher, “A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics,” Sensors (Basel, Switzerland), vol. 21(19), 2021.
K. Y. H. Lim, P. Zheng, and C.-H. Chen, “A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives,” Journal of Intelligent Manufacturing, vol. 31, no. 6, pp. 1313–1337, 2020, doi: 10.1007/s10845-019-01512-w.
R. S. Kenett and J. Bortman, “The digital twin in Industry 4.0: A wide‐angle perspective,” Quality and Reliability Engineering International, vol. 38, pp. 1357–1366, 2021.
S. Mihai et al., “Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects,” IEEE Communications Surveys & Tutorials, vol. 24, no. 2, pp. 2255–2291, 2022, doi: 10.1109/COMST.2022.3208773.
H. Amthiou, M. Arioua, and T. Benbarrad, “Digital Twins in Industry 4.0: A Literature Review,” in ITM Web of Conferences, 2023.
I. Halenár, M. Juhás, B. Juhásová, and D. Borkin, “Virtualization of Production Using Digital Twin Technology,” in 2019 20th International Carpathian Control Conference (ICCC), 2019.
G. Korovin, “The Opportunities For Using Digital Twins In Industry,” Transbaikal State University Journal, vol. 27, no. 8, pp. 124–133, 2021.
V. Kuts, T. Otto, Y. Bondarenko, and F. Yu, “Digital Twin: Collaborative Virtual Reality Environment for Multi-Purpose Industrial Applications,” in Volume 2B: Advanced Manufacturing, 2020.
F. Pires, A. Cachada, J. Barbosa, A. Moreira, and P. Leitão, “Digital Twin in Industry 4.0: Technologies, Applications and Challenges,” in 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), 2019, pp. 721–726. doi: 10.1109/INDIN41052.2019.8972134.
R. P. Rolle, V. O. Martucci, and E. P. Godoy, “Architecture for Digital Twin implementation focusing on Industry 4.0,” IEEE Latin America Transactions, vol. 18, pp. 889–898, 2020, doi: 10.1109/TLA.2020.9082917.
Descargas
Publicado
Cómo citar
Número
Sección
Altmetrics
Descargas
Licencia
Derechos de autor 2024 AiBi Revista de Investigación, Administración e Ingeniería
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
La revista ofrece acceso abierto bajo una Licencia Creative Commons Attibution License
Esta obra está bajo una licencia Creative Commons Attribution (CC BY 4.0).
