Power losses in machine tools are converted into thermal energy warming up components such as rotary table, tool holder, linear guide rails etc., and the machine tool structure. Due to this temperature change, thermo-elastic deformations of the machine tool structure occur which directly influence the position of the tool center point (TCP). Consequently, the accuracy of the machine deteriorates during the production process. The warmed-up parts or components need to be cooled; therefore, fluidic systems, such as cooling system, are installed to prevent this effect. In order to reduce the occurring thermo-elastic deformations and to enhance the production quality it is necessary to minimize the heat input. Previous research projects mainly focused on the energy demand of the machine tool and its main drives, reducing the energy consumption by developing more efficient components, and control strategies. However, the energy consumption of the fluidic systems has not yet been described in detail. Therefore, a detailed analysis of the existing fluidic system structures and their energy demand is necessary in order to ensure a uniform temperature distribution of the machine tool at minimal energy consumption. The main goal of this paper is to analyze the energy consumption of the fluidic system exemplified by two demonstrator machines. This investigation will help obtain the information concerning the energy demand of the fluidic system for these two different machines. This makes it possible to predict the consumed energy and the thermal behavior of the machine tool and its fluidic systems. Furthermore, the Energy consumption can serve as a reference for developing new system structures. Firstly, the paper describes the two different demonstrator machines with a special focus on their fluidic systems. Secondly, the methodology of the measurement to measure the energy consumption of the whole machine and of the fluidic systems is shown. Lastly, with the aid of experimental investigations the energy consumption of each machine is calculated and discussed for a defined process. As a result of the investigation, the energy distribution of the fluidic systems for both machines is determined. This knowledge serves as a reference for further investigations, for example, the cooling system. The first steps of the network-based simulation strategy are illustrated. These network-based models are helpful for future investigations regarding new structuring concepts such as the decentralization of the fluidic systems.
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