Sara Antomarioni

Università Politecnica delle Marche, Italy
PhD
Department of Industrial Engineering and Mathematical Science

Scopus ID:  57200248883
Researcher ID: AAO-3553-2020
Google Scholar profile: link
ORCID ID:  https://orcid.org/0000-0001-8584-7814

Selected Publications:

  1. Antomarioni, S., Ciarapica, F. E., Bevilacqua, M. (2022). Data-driven approach to predict the sequence of component failures: a framework and a case study on a process industry. International Journal of Quality & Reliability Management, 40 (3), 752–776. doi: https://doi.org/10.1108/ijqrm-12-2020-0413

  2. Fani, V., Antomarioni, S., Bandinelli, R., Ciarapica, F. E. (2023). Data Mining and Augmented Reality: An Application to the Fashion Industry. Applied Sciences, 13 (4), 2317. doi: https://doi.org/10.3390/app13042317

  3. Antomarioni, S., Lucantoni, L., Ciarapica, F. E., Bevilacqua, M. (2023). A Preliminary Implementation of Data-Driven TPM: A Real Case Study. Lecture Notes in Mechanical Engineering, 14–22. doi: https://doi.org/10.1007/978-3-031-25448-2_2

  4. Marcucci, G., Antomarioni, S., Ciarapica, F. E., Bevilacqua, M. (2021). The impact of Operations and IT-related Industry 4.0 key technologies on organizational resilience. Production Planning & Control, 33 (15), 1417–1431. doi: https://doi.org/10.1080/09537287.2021.1874702

  5. Crespo Márquez, A., de la Fuente Carmona, A., Antomarioni, S. (2019). A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency. Energies, 12 (18), 3454. doi: https://doi.org/10.3390/en12183454

  6. Antomarioni, S., Pisacane, O., Potena, D., Bevilacqua, M., Ciarapica, F. E., Diamantini, C. (2019). A predictive association rule-based maintenance policy to minimize the probability of breakages: application to an oil refinery. The International Journal of Advanced Manufacturing Technology, 105 (9), 3661–3675. doi: https://doi.org/10.1007/s00170-019-03822-y

  7. Ciarapica, F., Bevilacqua, M., Antomarioni, S. (2019). An approach based on association rules and social network analysis for managing environmental risk: A case study from a process industry. Process Safety and Environmental Protection, 128, 50–64. doi: https://doi.org/10.1016/j.psep.2019.05.037