Thermally compensated 5-axis machine tools evaluated with impeller machining tests

Abstract

Sustainable precision manufacturing requires a transformation from resource-based to intelligence-based reduction strategies for thermal errors. Current approaches such as machine cooling and air-conditioning of shop floors are highly energy intensive. Therefore, this paper presents and evaluates a comprehensive thermal error compensation strategy for 5-axis machine tools considering thermal errors of linear and rotary axes, as well as a wide variety of thermal load cases. The method encompasses an automatic characterization of the thermal behaviour of 5-axis machine tools as well as an automated setup and adaption of the data-driven compensation models to realize high robustness to changing thermal boundary conditions. The applied on-machine measurement cycle identifies 15 axis-specific thermal errors using a touch trigger probe and a precision sphere. To demonstrate the universal application and long-term robustness of the presented compensation strategy, it is evaluated on two different 5-axis machine tools using long-term experiments between 700 h and 900 h. The peak-to-peak values of the volumetric thermal errors at the considered working space positions are reduced from 76 µm to 20 µm and from 84 µm to 33 µm, respectively. This corresponds to a reduction of 74% and 61%. The corresponding root mean squared errors are reduced by 84% and 65%. Finally, the effectiveness of thermally compensated 5-axis machine tools is analysed during simultaneous 5-axis milling by manufacturing two uncompensated and two compensated impellers. The self-learning thermal error compensation reduces the maximum root mean squared error of the impeller blades up to 73% from 32 µm to 9 µm for temperature variations of about 10 °C. Thus, thermally compensated 5-axis machine tools increase the process capability in fluctuating ambient temperatures. Consequently, the self-learning thermal error compensation enables a significant increase in accuracy without requiring prior knowledge of the thermal machine tool behaviour. This provides a significant step towards more sustainable precision manufacturing.