Inteligencia Artificial para el soporte a la toma de decisiones en el ciclo de vida de los equipos industriales

Número

Núm. 85 (2025): DyO - 85 (Abril 2025)

Sección

Artículos
  • Miguel Ángel Mateo-Casalí Research Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022 Valencia, Spain.
  • Juan Pablo Fiesco Research Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022 Valencia, Spain.
  • Beatriz Andres Research Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022 Valencia, Spain.
  • Raul Poler Research Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022 Valencia, Spain.

DOI:

https://doi.org/10.37610/85.691

Publicado

16-04-2025

Resumen

Este artículo realiza una revisión de la literatura sobre las aplicaciones de la IA en cada fase del ciclo de vida de los equipos industriales, incluyendo el diseño, la fabricación el uso y la reparación/reutilización/reciclado. Sobre la base del proyecto europeo AIDEAS, un conjunto de soluciones basadas en IA se conceptualiza. Estas soluciones se contextualizan en un piloto industrial que se dedica a la fabricación de equipos para la inspección y clasificación de alimentos. Las líneas futuras de investigación se dirigen hacia el desarrollo de las tecnologías de IA conceptualizadas, y su implementación en el piloto industrial objeto de estudio.

Palabras clave:

Industria 4.0, Inteligencia Artificial, ciclo de vida, equipos industriales

Agencias de apoyo

  • La investigación que ha conducido a estos resultados ha recibido financiación del Programa Marco Horizonte Europa (HORIZONTE) con el acuerdo de subvención nº 101057294 "AI Driven Industrial Equipment Product Life Cycle Boosting Agility, Sustainability, and Resilience (AIDEAS)", de la Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital de la Generalitat Valenciana "Programa Investigo" (ref. INVEST/2022/330), apoyado por la Unión Europea - NextGenerationEU dentro del Plan de Recuperación, Transformación y Resiliencia.a

Referencias

AIDEAS. (2022). AI Driven Industrial Equipment Product Life Cycle Boosting Agility, Sustainability and Resilience. European Union’s Horizon Europe re-search and innovation programme under grant agreement No. 101057294. https://doi.org/10.3030/101057294

ALMOSLEHY, S. A. M., & ALKAHTANI, M. S. (2021). Key approaches, risks, and product performance in managing the development process of complex products sustainably. Sustainability (Switzerland), 13(9). https://doi.org/10.3390/su13094727

AYNETO GUBERt, X. (2019). La industria 4.0, el nuevo motor de la innovación industrial. Dirección y Organización, 69, 99–110. https://doi.org/10.37610/dyo.v0i69.563

BADUGE, S. K., THILAKARATHNA, S., PERERA, J. S., ARASHPOUR, M., SHARAFI, P., TEODOSIO, B., SHRINGI, A., & MENDIS, P. (2022). Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Automation in Construction, 141, 104440. https://doi.org/10.1016/j.autcon.2022.104440

BEN SALAH, Y., WEIGUO, L., AILING, T., SELLAMI, L., BEN HAMIDA, A., & ZARDOUMI, S. (2022). Optimal set up for manufacturing inspection system via mapping, and 3D scanning. PROCEEDINGS OF 2022 14TH INTERNATIONAL CONFERENCE ON MANAGEMENT OF DIGITAL ECOSYSTEMS, MEDES 2022, 70–73. https://doi.org/10.1145/3508397.3564829

BLOMSMA, F., & BRENNAN, G. (2017). The Emergence of Circular Economy: A New Framing Around Prolonging Resource Productivity. Journal of Industrial Ecology, 21(3), 603–614. https://doi.org/10.1111/jiec.12603

BOZA, A., ALARCÓN, F., PEREZ, D., & GÓMEZ-GASQUET, P. (2018). Industry 4.0 From the Supply Chain Perspective, 331–351. https://doi.org/10.4018/978-1-5225-4936-9.ch014

BUCHMEISTER, B., PALCIC, I., & OJSTERSEK, R. (2019). Artificial Intelligence in Manufacturing Companies and Broader: An Overview. 081–098. https://doi.org/10.2507/daaam.scibook.2019.07

CALVIN, T. W. (1984). Quality control techniques for “Zero Defects”: Thomas W. Calvin. IEEE Trans. Components Hybrids Mfg Technol.CHMT, 6(3), 323 (September 1983). Microelectronics Reliability, 24(5), 991–992. https://doi.org/10.1016/0026-2714(84)90075-1

CHANG, C. W., LEE, H. W., & LIU, C. H. (2018). A review of artificial intelligence algorithms used for smart machine tools. In Inventions, 3(3). MDPI Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/inventions3030041

CHEAH, C. G., CHIA, W. Y., LAI, S. F., CHEW, K. W., CHIA, S. R., & SHOW, P. L. (2022). Innovation designs of industry 4.0 based solid waste management: Machinery and digital circular economy. Environmental Research, 213. https://doi.org/10.1016/j.envres.2022.113619

ÇINAR, Z. M., NUHU, A. A., ZEESHAN, Q., KORHAN, O., ASMAEL, M., & SAFAEi, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability (Switzerland), 12(19). https://doi.org/10.3390/su12198211

CIOFFI, R., TRAVAGLIONI, M., PISCITELLI, G., PETRILLO, A., & DE FELICE, F. (2020). Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions. In Sustainability (Switzerland), 12(2). MDPI. https://doi.org/10.3390/su12020492

COMMISSION, E., INNOVATION, D.-G. FOR R. AND, BREQUE, M., DE NUL, L., & PETRIDIS, A. (2021). Industry 5.0 – Towards a sustainable, human-centric and resilient European industry. Publications Office of the European Union. https://doi.org/doi/10.2777/308407

DOSTATNI, E., DUDKOWIAK, A., ROJEK, I., & MIKOLAJEWSKI, D. (2023). Environmental analysis of a product manufactured with the use of an additive technology – AI-based vs. traditional approaches. Bulletin of the Polish Academy of Sciences Technical Sciences. https://doi.org/10.24425/bpasts.2023.144478

ELOUARIAGHLI, F. N., KOZDERKA, S. M., QUARANTA, T. G., PENA, F. D., ROSE, F. B., & HOARAU, S. Y. (2022). Eco-design and Life Cycle Management: Consequential Life Cycle Assessment, Artificial Intelligence and Green IT. IFAC-PapersOnLine, 55(5), 49–53. https://doi.org/10.1016/j.ifacol.2022.07.638

EUROPEAN COMMISSION. (2022). COM/2022/140 final. https://www.resourcepanel.org/es/reports/global-resources-outlook

FARBIZ, F., HABIBULLAH, M. S., HAMADICHAREF, B., MASZCZYK, T., & AGGARWAL, S. (2022). Knowledge-embedded machine learning and its applications in smart manufacturing. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-022-01973-6

FORTUNY-SANTOS, J., LÓPEZ, P. R. DE A., LUJÁN-BLANCO, I., & CHEN, P. K. (2020). Assessing the synergies between lean manufacturing and Industry 4.0. Direccion y Organizacion, 71, 71–86. https://doi.org/10.37610/dyo.v0i71.579

FRAGA-LAMAS, P., LOPES, S. I., & FERNÁNDEZ-CARAMÉS, T. M. (2021a). Green iot and edge AI as key technological enablers for a sustainable digital transition towards a smart circular economy: An industry 5.0 use case. Sensors, 21(17). https://doi.org/10.3390/s21175745

FRAGA-LAMAS, P., LOPES, S. I., & FERNÁNDEZ-CARAMÉS, T. M. (2021b). Green iot and edge AI as key technological enablers for a sustainable digital transition towards a smart circular economy: An industry 5.0 use case. Sensors, 21(17). https://doi.org/10.3390/s21175745

FRAILE, F., TAGAWA, T., POLER, R., & ORTIZ, A. (2018). Trustworthy Industrial IoT Gateways for Interoperability Platforms and Ecosystems. IEEE Internet of Things Journal, 5(6), 4506–4514. https://doi.org/10.1109/JIOT.2018.2832041

HADEA. (2023, May 11). Digital Product Passport. European Health and Digital Executive Agency.

HOU, J., SU, C., & WANG, W. (2008). Knowledge management in collaborative design. Proceedings of 2008 IEEE International Conference on Service Operations and Logistics, and Informatics, IEEE/SOLI 2008, 1, 848–852. https://doi.org/10.1109/SOLI.2008.4686517

ISO 14044:2006. (2006, July). Environmental management Life cycle assessment. SO/TC 207/SC 5. https://www.iso.org/obp/ui/#iso:std:iso:14044:ed-1:v1:es

JAVAID, M., HALEEM, A., SINGH, R. P., SUMAN, R., & GONZALEZ, E. S. (2022). Understanding the adoption of Industry 4.0 technologies in improving environmental sustainability. Sustainable Operations and Computers, 3(January), 203–217. https://doi.org/10.1016/j.susoc.2022.01.008

KARAYEL, D., OZKAN, S. S., & VATANSEVER, F. (2013). Integrated knowledge-based system for machine design. Advances in Mechanical Engineering, 2013. https://doi.org/10.1155/2013/702590

LI, B. HU, HOU, B. CUN, YU, W. TAO, LU, X. BING, & YANG, C. WEI. (2017). Applications of artificial intelligence in intelligent manufacturing: a review. In Frontiers of Information Technology and Electronic Engineering, 18(1), 86–96. Zhejiang University. https://doi.org/10.1631/FITEE.1601885

MATEO-CASALÍ, M. Á., FRAILE, F., BOZA, A., & POLER, R. (2023). A Maturity Model for Industry 4.0 Manufacturing Execution Systems. Springer International Publishing. https://doi.org/10.1007/978-3-031-29382-5_22

MENIRU, K., RIVARD, H., & BÉDARD, C. (2003). Specifications for computer-aided conceptual building design. Design Studies, 24(1), 51–71. https://doi.org/10.1016/S0142-694X(02)00009-1

NAZARENKO, A. A., SARRAIPA, J., CAMARINHA-MATOS, L. M., GRUNEWALD, C., DORCHAIN, M., & JARDIM-GONCALVEs, R. (2021). Analysis of relevant standards for industrial systems to support zero defects manufacturing process. In Journal of Industrial Information Integration, 23. Elsevier B.V. https://doi.org/10.1016/j.jii.2021.100214

NCHEKWUBE, D. C., FERRACUTI, F., FREDDI, A., IARLORI, S., LONGHI, S., & MONTERIU, A. (2022). Predictive Maintenance of Industrial Equipment using Deep Learning: from sensory data to remaining useful life estimation. 2022 IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022 - Proceedings, 624–629. https://doi.org/10.1109/MetroXRAINE54828.2022.9967582

NIU, X., WANG, M., & QIN, S. (2021). Product design lifecycle information model (PDLIM). The International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-021-07945-z/Published

ONGSULEE, P. (2017). Artificial intelligence, machine learning and deep learning. Fifteenth International Conference on ICT and Knowledge Engineering, 1–6. https://doi.org/10.1109/ICTKE.2017.8259629

POURNADER, M., GHADERI, H., HASSANZADEGAN, A., & FAHIMNIA, B. (2021). Artificial intelligence applications in supply chain management. In International Journal of Production Economics, 241. Elsevier B.V. https://doi.org/10.1016/j.ijpe.2021.108250

PRAUSE, M. (2019). Challenges of Industry 4.0 technology adoption for SMEs: The case of Japan. Sustainability (Switzerland), 11(20). https://doi.org/10.3390/su11205807

REN, S., ZHANG, Y., LIU, Y., SAKAO, T., HUISINGH, D., & ALMEIDA, C. M. V. B. (2019). A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions. Journal of Cleaner Production, 210, 1343–1365. https://doi.org/10.1016/j.jclepro.2018.11.025

RENDA, A., SCHWAAG SERGER, S., TATAJ, D., MORLET, A., ISAKSSON, D., MARTINS, F., MIR ROCA, M., HIDALGO, C., HUANG, A., DIXSON-DECLÈVE, S., BALLAND, P. A., BRIA, F., CHARVÉRIAT, C., DUNLOP, K., GIOVANNINI, E., & EUROPEAN COMMISSION. Directorate-General for Research and Innovation. (2021). Industry 5.0, a transformative vision for Europe: governing systemic transformations towards a sustainable industry.

RONAGHI, M. H. (2022). The influence of artificial intelligence adoption on circular economy practices in manufacturing industries. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-022-02670-3

SARIC, I., PERVAN, N., MUMINOVIC, A., & COLIC, M. (2018). Development of integrated intelligent cad system for design of shafts. Tehnicki Vjesnik, 25, 99–104. https://doi.org/10.17559/TV-20170521194820

SERRANO-RUIZ, J. C., MULA, J., & POLER, R. (2021). Smart manufacturing scheduling: A literature review. In Journal of Manufacturing Systems, 61, 265–287. Elsevier B.V. https://doi.org/10.1016/j.jmsy.2021.09.011

TAO, F., SUI, F., LIU, A., QI, Q., ZHANG, M., SONG, B., GUO, Z., LU, S. C. Y., & NEE, A. Y. C. (2019). Digital twin-driven product design framework. International Journal of Production Research, 57(12), 3935–3953. https://doi.org/10.1080/00207543.2018.1443229

TERZI, S., BOURAS, A., DUTTA, D., GARETTI, M., & KIRITSIS, D. (2010). Product lifecycle management - From its history to its new role. International Journal of Product Lifecycle Management, 4(4), 360–389. https://doi.org/10.1504/IJPLM.2010.036489

WANG, J., WU, H., & CHEN, Y. (2020). Made in China 2025 and manufacturing strategy decisions with reverse QFD. International Journal of Production Economics, 224, 107539. https://doi.org/10.1016/j.ijpe.2019.107539

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

ZAVALA-ALCÍVAR, A., VERDECHO, M. J., & ALFARO-SAIZ, J. J. (2023). Supply chain resilience: A conceptual evolution analysis. Direccion y Organizacion, 79, 5–17. https://doi.org/10.37610/dyo.v0i79.633

Licencia

Derechos de autor 2025 Miguel Ángel Mateo-Casalí, Juan Pablo Fiesco, Beatriz Andres, Raul Poler

Creative Commons License

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.

Licencia de Creative Commons
Esta obra se encuentra bajo una licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional.

Descargas

Los datos de descarga aún no están disponibles.