Sustainable peak power smoothing and energy-efficient machining process thorough analysis of high-frequency data. (10th October 2021) - Abdul Hadi, Muaaz; Brillinger, Markus; Wuwer, Marcel; Schmid, Johannes; Trabesinger, Stefan; Jäger, Markus; Haas, Franz
Abstract: The reduction of CO2 by moving from fossil to renewable energy sources is currently high on the agenda of many governments. Simultaneously these governments are also forcing the reduction of energy consumption. The primary focus of these agendas is on mobility, building, and industrial sectors. For the latter, energy-efficient shop floors and machining processes assist the reduction of energy consumption. Previous research has focused on energy-efficient machining strategies during machining processes. However, an energy-efficient start-up of these machines or their spindle axis start-up has been neglected until now. This paper focuses on this neglected issue by comparing the energy-efficiency, production time, and cost-efficiency of the CNC (computer numeric control) machine by varying the power input at the spindle axis. This is done by analysing the high-frequency data ( 500 H z ) of the machine from machining operations that is retrieved via the edge device. Concepts of data analytics and especially EDA (exploratory data analytics) were used to interactively visualize the inter-dependencies and develop results. It is shown that optimized reduction of spindle power input value leads to both: peak power smoothing from 20kW to 10kW and lowering of overall energy consumption by approximately 1.4%. Moreover, the costs and production time are marginally affected (0.518% and 0.523% respectively) by this optimized reduction of spindle power input value. Thus, this paper highlights a novel method from data acquisition to process improvement towards energy-efficient and sustainable machining. Graphical abstract: Highlights: Energy-efficient machining via innovative technique of data acquisition and analysis. Sustainable peak smoothing for spindle operations. Utilization of high-frequency machine data for enhancement of process.