Use of a digital twin for process optimization and predictive maintenance using the machine tools as example

The economic use of machine tools is highly dependent on the material removal rate and the amount of machine downtime. As a result, manufacturing companies focus on increasing feed rates and cutting depths and on reducing the number of necessary maintenance measures. The digital twin enables the optimization of machining processes, while obeying the static and dynamic load limits, through a permanent data exchange between the real machine tool and its virtual representation. Additionally, the acquisition of data during the lifetime of machine tools allows to detect changes in the dynamic behaviour of the feed drive components. Variations of the determined modal parameters indicate changes in the wear condition. By means of a probabilistic classification its future progression can be forecasted. © Carl Hanser Verlag GmbH & Co. KG.