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

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 & mu;m to 20 & mu;m and from 84 & mu;m to 33 & mu;m, 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 & mu;m to 9 & mu;m for temperature variations of about 10 & DEG;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. & COPY; 2023 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).