Las principales tecnologías de la era de la industria 5.0

The main technologies of the industry 5.0 era

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Luis Asunción Pérez-Domínguez
Resumen

En la actualidad el entorno industrial y la sociedad en general se encuentran en la dinámica de la Industria 4.0, la cual está sentando las bases para la próxima revolución industrial. A la par, las dificultades sanitarias mundial derivadas por el COVID-19 originando que las empresas busquen soluciones para seguir operando, esta situación de cualquier forma, provocando que la industria 5.0 dé un salto exponencial, haciendo que las empresas implementen nuevos procesos de fabricación. Por tanto, esta nueva revolución industrial consiste en aprovechar y desarrollar la inteligencia artificial para dar paso a la principal característica que la define, que es la colaboración entre el hombre y la máquina, trabajando juntos mientras las máquinas realizan las tareas más pesadas y repetitivas. De igual modo, las personas se encargan de monitorear las actividades. Adicionalmente, uno de los elementos fundamentales de I.5 son los cobots industriales (sistema robótico instituido para trabajar junto con los humanos) aunque los cobots y otros elementos independientemente del principal tema, también hay otros aspectos muy importantes como la sociedad 5.0 y la bioeconomía. De este modo, es por ello que en la presente investigación se tiene como objetivo principal en presentar las tecnologías transcendentales en la industria 5.0.

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K. A. Demir, G. Döven and B. Sezen, “Industry 5.0 and Human-Robot Co-working,” Procedia Comput. Sci., vol. 158, pp. 688–695, Jan. 2019, doi: 10.1016/j.procs.2019.09.104

M. Caggiano, C. Semeraro and M. Dassisti, “A Metamodel for Designing Assessment Models to support transition of production systems towards Industry 5.0,” Comput. Ind., vol. 152, p. 104008, Nov. 2023, doi: 10.1016/j.compind.2023.104008

P. K. R. Maddikunta et al., “Industry 5.0: A survey on enabling technologies and potential applications,” J. Ind. Inf. Integr., vol. 26, p. 100257, Mar. 2022, doi: 10.1016/j.jii.2021.100257

H. V. der L. Ulloa, “Revolución Industrial: una Revolución Técnica,” Rev. Estud., no. 9, Art. no. 9, 1991, doi: 10.15517/re.v0i9.29788

V. V. Martynov, D. N. Shavaleeva and A. A. Zaytseva, “Information Technology as the Basis for Transformation into a Digital Society and Industry 5.0,” in 2019 International Conference “Quality Management, Transport and Information Security, Information Technologies” (IT&QM&IS), Sep. 2019, pp. 539–543. doi: 10.1109/ITQMIS.2019.8928305

V. Özdemir and N. Hekim, “Birth of Industry 5.0: Making Sense of Big Data with Artificial Intelligence, ‘The Internet of Things’ and Next-Generation Technology Policy,” OMICS J. Integr. Biol., vol. 22, no. 1, pp. 65–76, Jan. 2018, doi: 10.1089/omi.2017.0194

M. Grzegorczyk, M. Mariniello, L. Nurski and T. Schraepen, “Blending the physical and virtual: A hybrid model for the future of work,” Bruegel Policy Contribution, Research Report 14/2021, 2021. [Online]. Available: https://www.econstor.eu/handle/10419/251067

A. Konovalov and C. C. Ruff, “Enhancing models of social and strategic decision making with process tracing and neural data,” WIREs Cogn. Sci., vol. 13, no. 1, p. e1559, 2022, doi: 10.1002/wcs.1559

M. Stella, “Cognitive Network Science for Understanding Online Social Cognitions: A Brief Review,” Top. Cogn. Sci., vol. 14, no. 1, pp. 143–162, 2022, doi: 10.1111/tops.12551

G. K. Deutsch et al., “Brief assessment of cognitive function in myotonic dystrophy: Multicenter longitudinal study using computer-assisted evaluation,” Muscle Nerve, vol. 65, no. 5, pp. 560–567, 2022, doi: 10.1002/mus.27520

Y. Chen, J. Elenee Argentinis and G. Weber, “IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research,” Clin. Ther., vol. 38, no. 4, pp. 688–701, Apr. 2016, doi: 10.1016/j.clinthera.2015.12.001

S. Katiyar and K. Katiyar, “Chapter 2 - Recent trends towards cognitive science: from robots to humanoids,” in Cognitive Computing for HumanRobot Interaction, M. Mittal, R. R. Shah, and S. Roy, Eds., in Cognitive Data Science in Sustainable Computing. , Academic Press, 2021, pp. 19–49. doi: 10.1016/B978-0-323-85769-7.00012-4

S. Wan, Z. Gu and Q. Ni, “Cognitive computing and wireless communications on the edge for healthcare service robots,” Comput. Commun., vol. 149, pp. 99–106, Jan. 2020, doi: 10.1016/j.comcom.2019.10.012

S. Gupta, A. K. Kar, A. Baabdullah and W. A. A. Al-Khowaiter, “Big data with cognitive computing: A review for the future,” Int. J. Inf. Manag., vol. 42, pp. 78–89, Oct. 2018, doi: 10.1016/j.ijinfomgt.2018.06.005

S. Wu, M. Wang and Y. Zou, “Bidirectional cognitive computing method supported by cloud technology,” Cogn. Syst. Res., vol. 52, pp. 615–621, Dec. 2018, doi: 10.1016/j.cogsys.2018.07.035

G. P. V. Arévalo, T. V. Pérez and H. F. C. Silva, “Digital transformation in state entities,” Rev. Ingenio, vol. 20, no. 1, pp. 53–58, 2023, doi: https://doi.org/10.22463/2011642X.3674

T. Q. Sun and R. Medaglia, “Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare,” Gov. Inf. Q., vol. 36, no. 2, pp. 368–383, Apr. 2019, doi: 10.1016/j.giq.2018.09.008

S. Fatima, K. C. Desouza and G. S. Dawson, “National strategic artificial intelligence plans: A multi-dimensional analysis,” Econ. Anal. Policy, vol. 67, pp. 178–194, Sep. 2020, doi: 10.1016/j.eap.2020.07.008

J. Ribeiro, R. Lima, T. Eckhardt and S. Paiva, “Robotic Process Automation and Artificial Intelligence in Industry 4.0 – A Literature review,” Procedia Comput. Sci., vol. 181, pp. 51–58, Jan. 2021, doi: 10.1016/j.procs.2021.01.104

F. Stella and J. Hughes, “The science of soft robot design: A review of motivations, methods and enabling technologies,” Front. Robot. AI, vol. 9, 2023, [Online]. Available: https://www.frontiersin.org/articles/10.3389/frobt.2022.1059026

M. Maroto-Gómez, F. Alonso-Martín, M. Malfaz, Á. Castro-González, J. C. Castillo and M. Á. Salichs, “A Systematic Literature Review of Decision-Making and Control Systems for Autonomous and Social Robots,” Int. J. Soc. Robot., vol. 15, no. 5, pp. 745– 789, May 2023, doi: 10.1007/s12369-023-00977-3

A. Amanian, A. Heffernan, M. Ishii, F. X. Creighton and A. Thamboo, “The Evolution and Application of Artificial Intelligence in Rhinology: A State of the Art Review,” Otolaryngol. Neck Surg., vol. 169, no. 1, pp. 21–30, 2023, doi: 10.1177/01945998221110076

J. M. Rožanec et al., “Human-centric artificial intelligence architecture for industry 5.0 applications,” Int. J. Prod. Res., vol. 61, no. 20, pp. 6847–6872, Oct. 2023, doi: 10.1080/00207543.2022.2138611

A. S. M. Sahan, S. Kathiravan, M. Lokesh and R. Raffik, “Role of Cobots over Industrial Robots in Industry 5.0: A Review,” in 2023 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), Jun. 2023, pp. 1–5. doi: 10.1109/ICAECA56562.2023.10201199

U. Kumar et al., “A systematic review of Industry 5.0 from main aspects to the execution status,” TQM J., vol. ahead-of-print, no. ahead-of-print, Jan. 2023, doi: 10.1108/TQM-06-2023-0183

R. R, R. R. Sathya, V. V, B. S and J. L. N, “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), Jun. 2023, pp. 1–6. doi: 10.1109/ICAECA56562.2023.10201120

M. Faccio et al., “Human factors in cobot era: a review of modern production systems features,” J. Intell. Manuf., vol. 34, no. 1, pp. 85–106, Jan. 2023, doi: 10.1007/s10845-022-01953-w

C. Taesi, F. Aggogeri and N. Pellegrini, “COBOT Applications—Recent Advances and Challenges,” Robotics, vol. 12, no. 3, Art. no. 3, Jun. 2023, doi: 10.3390/robotics12030079

R. A. Abdelouahid, O. Debauche and A. Marzak, “Internet of Things: a new Interoperable IoT Platform. Application to a Smart Building,” Procedia Comput. Sci., vol. 191, pp. 511–517, Jan. 2021, doi: 10.1016/j.procs.2021.07.066

N. Sharma, M. Shamkuwar and I. Singh, “The History, Present and Future with IoT,” in Internet of Things and Big Data Analytics for Smart Generation, V. E. Balas, V. K. Solanki, R. Kumar, and M. Khari, Eds., in Intelligent Systems Reference Library. , Cham: Springer International Publishing, 2019, pp. 27–51. doi: 10.1007/978-3-030-04203-5_3

K. Y. Sánchez-Mojica, L. A. Pérez-Domínguez, J. Gutiérrez Londoño and D. O. Cardozo Sarmiento, “A Data Analytic Monitoring with IoT System of the Reproductive Conditions of the Red Worm as a Product Diversification Strategy,” Appl. Sci., vol. 13, no. 18, Art. no. 18, Jan. 2023, doi: 10.3390/app131810522

J. Davis et al., “Smart Manufacturing,” Annu. Rev. Chem. Biomol. Eng., vol. 6, no. 1, pp. 141–160, 2015, doi: 10.1146/annurev-chembioeng-061114-123255

A. Kusiak, “Smart Manufacturing,” in Springer Handbook of Automation, S. Y. Nof, Ed., in Springer Handbooks. , Cham: Springer International Publishing, 2023, pp. 973–985. doi: 10.1007/978-3-030-96729-1_45

S. Tiwari, P. C. Bahuguna and R. Srivastava, “Smart manufacturing and sustainability: a bibliometric analysis,” Benchmarking Int. J., vol. 30, no. 9, pp.3281–3301, Jan. 2022, doi: 10.1108/BIJ-04-2022-0238

N. U. Huda, I. Ahmed, M. Adnan, M. Ali and F. Naeem, “Experts and intelligent systems for smart homes’ Transformation to Sustainable Smart Cities: A comprehensive review,” Expert Syst. Appl., vol. 238, p. 122380, Mar. 2024, doi: 10.1016/j.eswa.2023.122380

F. Ullah nd F. Al-Turjman, “A conceptual framework for blockchain smart contract adoption to manage real estate deals in smart cities,” Neural Comput. Appl., vol. 35, no. 7, pp. 5033–5054, Mar. 2023, doi: 10.1007/s00521-021-05800-6

M. Golovianko, V. Terziyan, V. Branytskyi and D. Malyk, “Industry 4.0 vs. Industry 5.0: Co-existence, Transition, or a Hybrid,” Procedia Comput. Sci., vol. 217, pp. 102–113, Jan. 2023, doi: 10.1016/j.procs.2022.12.206

M. Attaran, “The impact of 5G on the evolution of intelligent automation and industry digitization,” J. Ambient Intell. Humaniz. Comput., vol. 14, no. 5, pp. 5977–5993, May 2023, doi: 10.1007/s12652-020-02521-x

B. Alhayani et al., “5G standards for the Industry 4.0 enabled communication systems using artificial intelligence: perspective of smart healthcare system,” Appl. Nanosci., vol. 13, no. 3, pp. 1807–1817, Mar. 2023, doi: 10.1007/s13204-021-02152-4

A. Mehrish, N. Majumder, R. Bharadwaj, R. Mihalcea and S. Poria, “A review of deep learning techniques for speech processing,” Inf. Fusion, vol. 99, p. 101869, Nov. 2023, doi: 10.1016/j.inffus.2023.101869

J. Pan, J. Huang, G. Cheng and Y. Zeng, “Reinforcement learning for automatic quadrilateral mesh generation: A soft actor–critic approach,” Neural Netw., vol. 157, pp. 288–304, Jan. 2023, doi: 10.1016/j.neunet.2022.10.022

S. Civilibal, K. K. Cevik and A. Bozkurt, “A deep learning approach for automatic detection, segmentation and classification of breast lesions from thermal images,” Expert Syst. Appl., vol. 212, p.118774, Feb. 2023, doi: 10.1016/j.eswa.2022.118774

X. Li, P. Zheng, J. Bao, L. Gao and X. Xu, “Achieving Cognitive Mass Personalization via the Self-X Cognitive Manufacturing Network: An Industrial Knowledge Graph- and Graph Embedding-Enabled Pathway,” Engineering, vol. 22, pp. 14–19, Mar. 2023, doi: 10.1016/j.eng.2021.08.018

J. Vazquez-Armendariz et al., “Workflow for Robotic Point-of-Care Manufacturing of Personalized Maxillofacial Graft Fixation Hardware,” Integrating Mater. Manuf. Innov., vol. 12, no. 2, pp. 92–104, Jun. 2023, doi: 10.1007/s40192-023-00298-3

R. García-González, J. A. Paredes-Castañeda, y E. Bayona-Ibáñez, “DMAIC como herramienta para implementar un sistema de mejora para incrementar la productividad en la industria del sombrero,” Rev. Ingenio, vol. 20, no. 1, Art. no. 1, Jan. 2023, doi:

https://doi.org/10.22463/2011642X.3371

X. Zhang and X. Ming, “A Smart system in Manufacturing with Mass Personalization (S-MMP) for blueprint and scenario driven by industrial model transformation,” J. Intell. Manuf., vol. 34, no. 4, pp. 1875–1893, Apr. 2023, doi: 10.1007/s10845-021-01883-z

S. E. Barykin et al., “Smart City Logistics on the Basis of Digital Tools for ESG Goals Achievement,” Sustainability, vol. 15, no. 6, Art. no. 6, Jan. 2023, doi: 10.3390/su15065507

E. Flores-García, Y. Jeong, S. Liu, M. Wiktorsson, and L. Wang, “Enabling industrial internet of things-based digital servitization in smart production logistics,” Int. J. Prod. Res., vol. 61, no. 12, pp. 3884–3909, Jun. 2023, doi: 10.1080/00207543.2022.2081099

R. Pereira and N. dos Santos, “Neoindustrialization—Reflections on a New Paradigmatic Approach for the Industry: A Scoping Review on Industry 5.0,” Logistics, vol. 7, no. 3, Art. no. 3, Sep. 2023, doi: 10.3390/logistics7030043

B. Alojaiman, “Technological Modernizations in the Industry 5.0 Era: A Descriptive Analysis and Future Research Directions,” Processes, vol. 11, no. 5, Art. no. 5, May 2023, doi: 10.3390/pr11051318

S. Rajumesh, “Promoting sustainable and humancentric industry 5.0: a thematic analysis of emerging research topics and opportunities,” J. Bus. SocioEcon. Dev., vol. ahead-of-print, no. ahead-of-print, Jan. 2023, doi: 10.1108/JBSED-10-2022-0116

X. Wang et al., “Steps Toward Industry 5.0: Building ‘6S’ Parallel Industries With Cyber-Physical-Social Intelligence,” IEEECAA J. Autom. Sin., vol. 10, no. 8, pp. 1692–1703, Aug. 2023, doi: 10.1109/JAS.2023.123753

L. Gomathi, A. K. Mishra, and A. K. Tyagi, “Industry 5.0 for Healthcare 5.0: Opportunities, Challenges and Future Research Possibilities,” in 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI), Apr. 2023, pp. 204–213.

doi: 10.1109/ICOEI56765.2023.10125660

S. Ray, E. V. Korchagina, R. U. Nikam, and R. K. Singhal, “A Blockchain-based Secure Healthcare Solution for Poverty-led Economy of IoMT Under Industry 5.0,” in Inclusive Developments Through Socio-economic Indicators: New Theoretical and Empirical Insights, R. Chandra Das, Ed., Emerald Publishing Limited, 2023, pp. 269–280. doi: 10.1108/978-1-80455-554-520231022

A. Selvam, T. Aggarwal, M. Mukherjee, and Y. K. Verma, “Humans and robots: Friends of the future? A bird’s eye view of biomanufacturing industry 5.0,” Biotechnol. Adv., vol. 68, p. 108237, Nov. 2023, doi: 10.1016/j.biotechadv.2023.108237

S. Dalal, B. Seth, and M. Radulescu, “Driving Technologies of Industry 5.0 in the Medical Field,” in Digitalization, Sustainable Development, and Industry 5.0, B. Akkaya, S. Andreea Apostu, E. Hysa, and M. Panait, Eds., Emerald Publishing Limited, 2023, pp.

–292. doi: 10.1108/978-1-83753-190-520231014

M. Khan, A. Haleem, and M. Javaid, “Changes and improvements in Industry 5.0: A strategic approach to overcome the challenges of Industry 4.0,” Green Technol. Sustain., vol. 1, no. 2, p. 100020, May 2023, doi: 10.1016/j.grets.2023.100020

S. Yin and Y. Yu, “An adoption-implementation framework of digital green knowledge to improve the performance of digital green innovation practices for industry 5.0,” J. Clean. Prod., vol. 363, p. 132608, Aug. 2022, doi: 10.1016/j.jclepro.2022.132608

N. Bijon, T. Wassenaar, G. Junqua, and M. Dechesne, “Towards a Sustainable Bioeconomy through Industrial Symbiosis: Current Situation and Perspectives,” Sustainability, vol. 14, no. 3, Art. no. 3, Jan. 2022, doi: 10.3390/su14031605

W. Y. Cheah, R. P. Siti-Dina, S. T. K. Leng, A. C. Er, and P. L. Show, “Circular bioeconomy in palm oil industry: Current practices and future perspectives,” Environ. Technol. Innov., vol. 30, p. 103050, May 2023, doi: 10.1016/j.eti.2023.103050

B. Rethinam, R. Palanichamy, and J. D. John Britto, “Analysis of Batch Kinetic Data of Biodecolorization Reaction: Theoretical Approach for the Design of Packed Bed Reactor,” J. Environ. Eng., vol. 149, no. 10, p. 04023056, Oct. 2023, doi: 10.1061/JOEEDU.

EEENG-7269

R. Sindhwani, S. Afridi, A. Kumar, A. Banaitis, S. Luthra, and P. L. Singh, “Can industry 5.0 revolutionize the wave of resilience and social value creation? A multi-criteria framework to analyze enablers,” Technol. Soc., vol. 68, p. 101887, Feb. 2022, doi: 10.1016/j.techsoc.2022.101887

G. A. V. Clavijo y A. M. G. Bayona, “Ciudad Inteligente: mejoramiento de la seguridad ciudadana a través del uso de nuevas tecnologías,” Rev. Ingenio, vol. 20, no. 1, pp. 32–39, 2023, doi: https://doi.org/10.22463/2011642X.3510

F. Ince, “Socio-Ecological Sustainability Within the Scope of Industry 5.0,” in Implications of Industry 5.0 on Environmental Sustainability, IGI Global, 2023, pp. 25–50. doi: 10.4018/978-1-6684-6113-6.ch002

B. C. Quintero y W. A. D. Neira, “Habilidades de pensamiento computacional en niños y niñas de las escuelas primarias utilizando tecnologías 4.0: un análisis bibliométrico,” Rev. Ingenio, vol. 20, no. 1, pp. 40–45, 2023, doi: https://doi.org/10.22463/2011642X.3603

D. Romero and J. Stahre, “Towards The Resilient Operator 5.0: The Future of Work in Smart Resilient Manufacturing Systems,” Procedia CIRP, vol.104, pp. 1089–1094, Jan. 2021, doi: 10.1016/j.procir.2021.11.183

S. Chourasia, A. Tyagi, Q. Murtaza, R. S. Walia, and P. Sharma, “A Critical Review on Industry 5.0 and Its Medical Applications,” in Advances in Modelling and Optimization of Manufacturing and Industrial Systems, R. P. Singh, M. Tyagi, R. S. Walia, and

J. P. Davim, Eds., in Lecture Notes in Mechanical Engineering. Singapore: Springer Nature, 2023, pp. 251–261. doi: 10.1007/978-981-19-6107-6_18

R. Tallat et al., “Navigating Industry 5.0: A Survey of Key Enabling Technologies, Trends, Challenges, and Opportunities,” IEEE Commun. Surv. Tutor., pp. 1–1, 2023, doi: 10.1109/COMST.2023.3329472

J. Pizoń and A. Gola, “Human–Machine Relationship—Perspective and Future Roadmap for Industry 5.0 Solutions,” Machines, vol. 11, no. 2, Art. no. 2, Feb. 2023, doi: 10.3390/machines11020203

I. Yaqoob, K. Salah, R. Jayaraman, and M. Omar, “Metaverse applications in smart cities: Enabling technologies, opportunities, challenges, and future directions,” Internet Things, vol. 23, p. 100884, Oct. 2023, doi: 10.1016/j.iot.2023.100884

C. Jiang, C. Fu, Z. Zhao, and X. Du, “Effective Anomaly Detection in Smart Home by Integrating Event Time Intervals,” Procedia Comput. Sci., vol. 210, pp. 53–60, Jan. 2022, doi: 10.1016/j.procs.2022.10.119

J. Wang, R. Wang, H. Cai, L. Li, and Z. Zhao, “Smart household electrical appliance usage behavior of residents in China: Converging the theory of planned behavior, value-belief-norm theory and external information,” Energy Build., vol. 296, p. 113346, Oct.

, doi: 10.1016/j.enbuild.2023.113346

J. Vanus, R. Hercik, and P. Bilik, “Using Interoperability between Mobile Robot and KNX Technology for Occupancy Monitoring in Smart Home Care,” Sensors, vol. 23, no. 21, Art. no. 21, Jan. 2023, doi: 10.3390/s23218953

I. Froiz-Míguez, P. Fraga-Lamas, and T. M. FernándezCaraméS, “Design, Implementation, and Practical Evaluation of a Voice Recognition Based IoT Home Automation System for Low-Resource Languages and Resource-Constrained Edge IoT Devices: A

System for Galician and Mobile Opportunistic Scenarios,” IEEE Access, vol. 11, pp. 63623–63649, 2023, doi: 10.1109/ACCESS.2023.3286391

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