Investigating Predictive Maintenance Strategies for CNC Machine Tools in the Industry 4.0 Era
محورهای موضوعی : Manufacturing planning, optimization and simulation
Fionn Foley
1
(School of Science, Technology, Engineering and Mathematics, Munster Technological University, Clash, Tralee, Co. Kerry, Ireland, V92 CX88)
کلید واژه: Retrofit, Maintenance, Internet of Things, Industry 4.0, CNC Machine, Predictive Maintenance,
چکیده مقاله :
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.
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.
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