Abstract
The article discusses the importance of smart production during the progress of Industry 4.0 and the challenges that Big Data analytics and artificial intelligence (AI) tools face. Using AI tools, such as predictive maintenance, production optimisation and quality control systems, can improve production efficiency, quality and safety. This article also highlights the goals of AI technologies, such as reducing production downtimes, optimising production, improving product quality and safety, and increasing automation to achieve the zero-defect philosophy. It concludes that applying AI solutions can help to reduce defects, waste and errors in production processes, which will result in increasing the efficiency and quality of production processes.
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References
AIDEAS. AI Driven Industrial Equipment Product Life Cycle Boosting Agility, Sustainability and Resilience. European Union’s Horizon Europe research and innovation programme under grant agreement No. 101057294 (2022)
Calvin, T.W.: Quality control techniques for zero defects. Tech. Pap. -Soc. Manufact. Eng. C(3), 323–328 (1983). https://doi.org/10.1016/0026-2714(84)90075-1
Lin, J.W., Liao, S.W., Leu, F.Y.: Sensor data compression using bounded error piecewise linear approximation with resolution reduction. Energies 12(13), 2523 (2019). https://doi.org/10.3390/en12132523
Moeuf, A., Lamouri, S., Pellerin, R., Tamayo-Giraldo, S., Tobon-Valencia, E., Eburdy, R.: Identification of critical success factors, risks and opportunities of industry 4.0 in SMEs. Int. J. Prod. Res. 58(5), 1384–1400 (2020). https://doi.org/10.1080/00207543.2019.1636323
Nazarenko, A.A., Sarraipa, J., Camarinha-Matos, L.M., Grunewald, C., Dorchain, M., Jardim-Goncalves, R.: Analysis of relevant standards for industrial systems to support zero defects manufacturing process. J. Ind. Inf. Integr. 23(September 2020), 100214 (2021). https://doi.org/10.1016/j.jii.2021.100214
Sanders, A., Elangeswaran, C., Wulfsberg, J.: Industry 4.0 implies lean manufacturing: research activities in industry 4.0 function as enablers for lean manufacturing. J. Ind. Eng. Manag.-JIEM 9(3), 811–833 (2016). https://doi.org/10.3926/jiem.1940
Serrano-Ruiz, J.C., Mula, J., Poler, R.: Smart manufacturing scheduling: a literature review. In: Journal of Manufacturing Systems (Vol. 61, pp. 265–287). Elsevier B.V (2021). https://doi.org/10.1016/j.jmsy.2021.09.011
Shafiq, M., Thakre, K., Krishna, K.R., Robert, N.J., Kuruppath, A., Kumar, D.: Continuous quality control evaluation during manufacturing using supervised learning algorithm for Industry 4.0. In: International Journal of Advanced Manufacturing Technology (2023). https://doi.org/10.1007/s00170-023-10847-x
Yu, W., Liu, Y., Dillon, T., Rahayu, W., Mostafa, F.: An integrated framework for health state monitoring in a smart factory employing IoT and big data techniques. IEEE Internet Things J. 9(3), 2443–2454 (2022). https://doi.org/10.1109/JIOT.2021.3096637
Acknowledgements
The research that led to these findings received funding from two sources. The first source of funding was from the Horizon Europe Framework Programme (HORIZON) with Grant Agreement No. 101057294 “AI Driven Industrial Equipment Product Life Cycle Boosting Agility, Sustainability, and Resilience (AIDEAS)”. The second source of funding was from the Regional Department of Innovation, Universities, Science, and Digital Society of the Generalitat Valenciana “Programa Investigo” (ref. INVEST/2022/330), which the European Union supported - NextGenerationEU under the Plan de Recuperación, Transformación y Resiliencia.
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Mateo-Casali, M.Á., Fiesco, J.P., Andres, B., Poler, R. (2024). Optimising Machinery Utilisation by Applying Artificial Intelligence. In: Bautista-Valhondo, J., Mateo-Doll, M., Lusa, A., Pastor-Moreno, R. (eds) Proceedings of the 17th International Conference on Industrial Engineering and Industrial Management (ICIEIM) – XXVII Congreso de Ingeniería de Organización (CIO2023). CIO 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 206. Springer, Cham. https://doi.org/10.1007/978-3-031-57996-7_76
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