MoTeF 4.0


Development of a mobile fluid dosing system with connection to digital production planning systems for automatic, demand-oriented and documented replenishment of technical fluids at machine tools

Stylized representation of the MoTeF project © Yannick Dassen  


In machine tools, technical fluids are used for lubrication, cooling and chip removal. Typical representatives of technical fluids are cooling lubricants (MWF), which contribute to better dimensional accuracy and surface quality of the machined workpieces and reduce tool wear. Although coolant lubricants account for a large proportion of the operating costs of machine tools, the maintenance and care of this operating fluid is often neglected in the industrial environment. Especially in smaller companies with up to 20 machine tools, checking is done manually and often according to gut feeling instead of fixed procedures. The entire process is usually not properly logged, making it difficult to analyze coolant consumption for early detection or prediction of problems. Current systems for individual fluid management often only address specific problems and therefore cannot be easily used on multiple, heterogeneous machine tools. In addition, the available solutions generally do not offer digital interfaces that can be used to implement end-to-end networking and connection to existing production control systems (MES).



This insufficient situation is being addressed in the MoTeF 4.0 project with the development of a networked, mobile mixing system for technical fluids that enables automatic refilling depending on the planned production. The newly developed mixing system detects the machine tool, measures the fill level, processes sensor data on the condition of the cooling lubricant, and enables refilling into the machine tank according to the target parameters of the production control system (MES). The test parameters of the cooling lubricant (temperature, pH value and nitrite content) as well as the delivered quantity and concentration are logged for each machine. By evaluating the data recorded in this process, errors are detected at an early stage by means of predictive maintenance and maintenance measures are recommended.