An accuracy evolution method applied to five-axis machining of curved surfaces

Currently, some high-value-added applications involve the manufacturing of curved surfaces, where it is challenging to achieve surface accuracy, repeatability, and productivity simultaneously. Among free-form surfaces, curved surfaces are commonly used in blades and airfoils (with a teardrop-shaped cross-section) and optical systems (with axial symmetry). In both cases, multi-axis milling accuracy directly affects the subsequent process step. Therefore, reducing even insignificant errors during machining can improve the accuracy in the final production stages. This study proposes an “evolution” method to improve the machining accuracy of curved surfaces. The key is to include compensation for the machining error after the first part through profile error measurement. Thus, correction can be applied directly after the manufacturing programming is fully developed, achieving the product with the minimum number of iterations. Accordingly, this method measures the machining error and changes only one key parameter after the process. This study considered two cases. First, an airfoil in which the clamping force was corrected; the results were quite good with only one modification in the blade machining case. Second is an aspherical surface where tool path correction in the Z-axis was applied; the error was effectively compensated along the normal vector of the workpiece surface. The experimental results showed that the surface accuracy increased from 44.4 to 4.5 μm, and the error was reduced by 89.9%, confirming that the accuracy of the machine tool and process had achieved “evolution.” This technical study is expected to help improve the quality and productivity of manufacturing highly accurate curved surfaces. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was supported by: Natural Science Foundation of Shaanxi Province (Grant number: 2021JM010) Natural Science Foundation of Suzhou City (Grant number: SYG202018) Spanish Ministry of science and innovation (Grant number: RTC2019-007,194–4) funded by MCIN/AEI/ 10.13039/501100011033 Basque government group IT 1573-22 Fundamental Research Funds for the Central Universities (Grant No. xzy012019007) Project ITENEO Grant PID2019-109340RB-I00 funded by MCIN/AEI/ 10.13039/501100011033 Project HCTM Grant PDC2021-121792-100 funded by 702 MCIN/AEI/ 10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR The Basque Government Department of Education for the pre-doctoral grant PRE_2021_1_0142