Investigating Predictive Maintenance Strategies for CNC Machine Tools in the Industry 4.0 Era
الموضوعات :
Fionn Foley
1
(School of Science, Technology, Engineering and Mathematics, Munster Technological University, Clash, Tralee, Co. Kerry, Ireland, V92 CX88)
الکلمات المفتاحية: Retrofit, Maintenance, Internet of Things, Industry 4.0, CNC Machine, Predictive Maintenance,
ملخص المقالة :
This research compares different approaches for achieving precise predictive maintenance (PdM) results from CNC machine tools. For smaller enterprises, it is crucial to be able to upgrade their existing industrial machines with industry 4.0 systems, as the global marketplace becomes more competitive. The evolution of the Internet of Things (IoT) and the creation of cyber-physical systems (CPS) in the industry has enabled big data generation. New maintenance methodologies have emerged where decisions are driven by mathematical models and data analysis. In this study, a low-cost strategy is used as a baseline to establish system accuracy. Results are then compared to a more comprehensive strategy outlining the potentials of these retrofit predictive maintenance (PdM) systems. Numerous data modeling techniques were employed to increase system accuracy while focusing on the remaining useful life (RUL) of the cutting tool. Through analysis of the selected strategies favorable correlation is evident between predicted results and physical tool wear, optimal accuracy of 95.68% is achieved by utilizing hybrid data modeling techniques. The study highlights the possibilities for enterprises looking to adopt PdM strategies and consequently capitalize on the potential of Industry 4.0.
[1] Sezer, E., Romero, D., Guedea, F., Macchi, M. and Emmanouilidis, C. 2018. An industry 4.0-enabled low cost predictive maintenance approach for SMEs. Proceeding of 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC).
[2] Luo, W.H., Tianliang, Ye, Y., Zhang, C. and Wei, Y. 2020. A hybrid predictive maintenance approach for CNC machine tool driven by digital twin. Robotics and Computer Integrated Manufacturing. 65: 101974. doi:10.1016/j.rcim.2020.101974.
[3] Bousdekis, A., Papageorgiou, N., Magoutas, B., Apostolou, D. and Mentzas, G. 2018. Enabling condition-based maintenance decisions with proactive event-driven computing. Computers in Industry. 100: 173-183. doi: 10.1016/j.compind.2018.04.019.
[4] Ruschel, E., Santos, E.A.P. and Loures, E.d.F.R. 2017. Industrial maintenance decision-making: A systematic literature review. Journal of Manufacturing Systems. 45: 180-194. doi: 10.1016/j.jmsy.2017.09.003.
[5] Garg, A. and Deshmukh, S.G. 2006. Maintenance management: Literature review and directions. Journal of Quality in Maintenance Engineering. 12(3): 205-238. doi: 10.1108/13552510610685075.
[6] Lee, W.J., Wu, H., Yun, H., Kim, H., Jun, M.B.G. and Sutherland, J.W. 2019. Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data. Procedia CIRP. 80: 506-511. doi:10.1016/j.procir.2018.12.019.
[7] Kang, H.S., Lee, J.Y., Choi, S., Kim, H., Park, J.H., Son, J.Y., Kim, B.H. and Noh, S.D. 2016. Smart manufacturing: Past research, present findings, and future directions. International Journal of Precision Engineering and Manufacturing-Green Technology. 3(1): 111-128. doi: 10.1007/s40684-016-0015-5.
[8] Arjoni, D.H., Madani, F.S., Ikeda, G., Carvalho, G.d.M., Cobianchi, L.B., Ferreira, L.F.L.R. and Villani, E. 2017. Manufacture equipment retrofit to allow usage in the industry 4.0. Proc. 2nd International Conference on Cybernetics, Robotics and Control (CRC).
[9] Kagermann, H., Helbig, J. and Wahlster, W. 2013. Recommendations for implementing the strategic initiative Industrie 4.0: Securing the Future of German Manufacturing Industry; Final Report of the Industrie 4.0 Working Group. Forschungsunion, Germany.
[10] Adu-Amankwa, K., Attia, A.K.A., Janardhanan, M.N. and Patel, I. 2019. A predictive maintenance cost model for CNC SMEs in the era of Industry 4.0. The International Journal of Advanced Manufacturing Technology. 104(9): 3567-3587. doi: 10.1007/s00170-019-04094-2.
[11] Toh, K.T.K. and Newman, S.T. 1996. The future role of DNC in metalworking SMEs. International Journal of Production Research. 34(3): 863-877. doi: 10.1080/00207549608904938.
[12] Hoffmann Souza, M.L., da Costa, C.A., de Oliveira Ramos, G. and da Rosa Righi, R. 2020. A survey on decision-making based on system reliability in the context of industry 4.0. Journal of Manufacturing Systems. 56: 133-156. doi: 10.1016/j.jmsy.2020.05.016.
