In recent years, the automotive industry has set ambitious goals to reduce CO2 emissions of production facilities and started to consider energy costs in strategic procurement of manufacturing systems. Based on an industrial use-case, energy and media consumption behavior of production lines for series production of powertrain components was characterized and the degree of energetic utilization of machines and peripherals was evaluated. Technical and organizational efficiency measures were identified and benchmark KPIs were defined. In order to allow for demand forecasting as well as an evaluation of optimization strategies, material flow models of the production lines were extended by energy and media consumption data. Thus, demand-oriented operation of given infrastructure in different production scenarios as well as optimal dimensioning, design and control of new infrastructure was facilitated. Finally, a monitoring system based on an aggregation of continuously recorded sensor and production control data was established, allowing for continuous target-actual KPI comparison.

In times of unstable market development due to the energy system transformation and legislative measures concerning the reduction of CO2 emissions, the manufacturing industry is increasingly aware of the ecological and economical importance of the factor energy. A considerable share of industrial energy and resource consumption can be attributed to machine tools in general and grinding machines in particular. Grinding is an essential technology used for finishing operations of many precision components, especially such made of hard and brittle materials. This work presents an investigation in the energy consumption related to high-performance grinding processes. Grinding tests were performed using different grinding strategies and abrasives including corundum (Al2O3) and CBN. In order to identify the dynamic process behavior and energy flows, process parameters were varied and electrical power consumption of the CNC grinding machine, its drive system as well as different peripherals such as cooling lubricant pumps were measured. Specific energy consumption was determined as a function of material removal rate and compared to results of milling and turning processes. The key influence factors on grinding energy efficiency and productivity are depicted. Strategies are evaluated to optimize the overall process performance from an energetic point of view.

This work presents an approach to determine relevant energy efficiency and productivity KPIs of machining processes based on a real-time interpretation of sensor data and machine control data. A comparison of the actual power consumption during machining with an energetic model of the load-free condition enables the calculation of energetic efficiency and primary processing time. The approach was tested on a CNC turning and milling center equipped with power meters and compressed air sensors. Sensor data as well as relevant machine control data are read, processed and recorded via SCADA software in order to automatically calculate certain KPIs.