References
- Beregi, R., Pedone, G., Háy, B., Váncza, J. “Manufacturing Execution System integration through the standardization of a common service model for Cyber-Physical Production System”, Applied Sciences 11, 7581, 2021. DOI: 10.3390/app11167581
- Cadavid, J. P. U., Lamouri, S., Grabot, B., Pellerin, R., Fortin, R. “Machine learning applied in production planning and control: A state-of-the-art in the era of Industry 4.0”, Journal of Intelligent Manufacturing 31, pp. 1531 – 1558, 2020. DOI: 10.1007/s10845-019-01531-7
- Zhou, Z.H. “Machine Learning”, Springer Nature: Singapore, 2021. DOI: 10.1007/978-981-15-1967-3
- Prasetyawan, Y., Giffary, F., Putera, B. S. A. “The proposed OEE - Sigma prediction for increased profits”, Materials Science and Engineering 847, pp. 1 – 9, 2020. DOI: 10.1088/1757-899X/847/1/012034
- Arinez, J. F., Chang, Q., Gao, R. X., Xu, C., Zhang, J. “Artificial intelligence in advanced manufacturing: Current status and future outlook”, Journal of Manufacturing Science and Engineering 142, pp. 1 – 16, 2020. DOI: 10.1115/1.4047855
- Omar, F., Alexandre, K., Alekcie S. “Predicting the Burnishing Force for Cylindrical Workpieces with Amodified Surface Layer”, Journal of Mechanical Engineering 72, pp. 35 – 48, 2022. DOI: 10.2478/scjme-2022-0004
- Plathottam, S. J., Rzonca, A., Lakhnori, R., Iloeje, C. O. “A review of artificial intelligence applications in manufacturing operations”, Journal of Advanced Manufacturing and Processing 5 (3), pp. 1 – 19, 2023. DOI: 10.1002/amp2.10159
- Song, Z., Luo, S. “Application of machine learning and data mining in manufacturing Industry”, International Journal of Computer Science and Information Technology 2 (1), pp. 425 – 436, 2024. DOI: 10.62051/ijcsit.v2n1.45
- Akshansh, M. “Machine Learning Algorithm for Surface Quality Analysis of Friction Stir Welded Joint”, Strojnícky časopis – Journal of Mechanical Engineering 70 (2), pp. 11 – 20, 2020. DOI: 10.2478/scjme-2020-0016
- Chen, T., Sampath, V., May, M. C., Shan, S., Jorg, O. J., Aguilar Martín, J. J., Stamer, F., Fantoni, G., Tosello, G., Calaon, M. “Machine learning in manufacturing towards, Industry 4.0: From ’For Now’ to ’Four-Know’”, Applied Sciences 13, pp. 1 – 32, 2023. DOI: 10.3390/app13031903
- Jordan, M. I., Mitchell, T. M. “Machine learning: Trends, perspectives, and prospects”, Science 349, pp. 255 – 260, 2015. DOI: 10.1126/science.aaa8415
- Nakajima, S. “Introduction to TPM: Total Productive Maintenance”, Productivity Press: Cambridge, UK, 1988.
- Mittal, S., Khan, M. A., Romero, D., Wuest, T. “Smart manufacturing: Characteristics, technologies and enabling factors”, Proceedings of the Institution of Mechanical Engineers, Journal of Engineering Manufacture 233, pp. 1342 – 1361, 2019. DOI: 10.1177/0954405417736547
- Anusha, C. H., Umasankar, V. “Performance prediction through OEE-model”, International Journal of Industrial Engineering and Management 11, pp. 93 – 103, 2020. DOI: 10.24867/IJIEM-2020-2-256
- Lepenioti, K., Pertselakis, M., Bousdekis, A., Louca, A., Lampathaki, F., Apostolou, D., Mentzas, G., Anastasiou, S. “Machine learning for predictive and prescriptive analytics of operational data in smart manufacturing”, Advanced Information Systems Engineering Workshop. Springer International Publishing, Cham, pp. 5 – 16, 2020. DOI: 10.1007/978-3-030-49165-9_1
- Okpala, C. C., Anozie, S. C., Mgbemena, C. E. “The optimization of overall equipment effectiveness factors in a pharmaceutical company”, Heliyon 6 (4), pp. 1 – 9, 2020. DOI: 10.1016/j.heliyon.2020.e03796
- Souza, B. V., Santos, S. R. B., Oliveira, A. M., Givigi, S. N. “Analyzing and predicting Overall Equipment Effectiveness in manufacturing industries using machine learning”, In Proceedings of the 2022 IEEE International Systems Conference (SysCon), Montreal, QC, Canada, 25–28 April, pp. 1 – 8, 2022. DOI: 10.1109/SysCon53536.2022.9773846
- El Mazgualdi, C., Masrour, T., El Hassani, I., Khdoudi, A. “Machine learning for KPIs prediction: A case study of the overall equipment effectiveness within automotive industry”, Soft Computing. 25, pp. 2891 – 2909, 2021. DOI: 10.1007/s00500-020-05348-y
- El Mazgualdi, C., Masrour, T., ElHassani, I., Khdoudi, A. “Using machine learning for predicting efficiency in manufacturing industry”, In Advanced Intelligent Systems for Sustainable Development, Springer International Publishing, Cham, pp. 750 – 762, 2020. DOI: 10.1007/978-3-030-36671-1_68
- Imane, M., Aoula, E. S., Achouyab, E. H. “Using Bayesian Ridge Regression to predict the Overall Equipment Effectiveness performance”, In Proceedings of the 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), Meknes, Morocco, 3–4 March 2022; pp. 1 – 4, 2022. DOI: 10.1109/IRASET52964.2022.9738316
- Imane, M., Aoula, E. S., Achouyab, E .H. “Support Vector Regression to predict the Overall Equipment Effectiveness indicator”, In Proceedings of the 2022 International Conference on Intelligent Systems and Computer Vision (ISCV), Fez, Morocco, 18–22 May 2022; pp. 1 – 5, 2022. DOI: 10.1109/ISCV54655.2022.9806111
- Dobra, P., Jósvai, J. “Cumulative and rolling horizon prediction of Overall Equipment Effectiveness (OEE) with machine learning”, Big Data and Cognitive Computing 7, pp. 1 – 16, 2023. DOI: 10.3390/bdcc7030138