Precision-cast blades are core components in aeroengines, and their machining precision has a considerable effect on the performance of the aeroengine. In this paper, an adaptive grinding method for precision-cast blades with geometric deviation is proposed to improve the machining precision, machining efficiency, and automation level of precision-cast blade grinding. A scheme for adaptive grinding of precision-cast blades with geometric deviation is developed, a grinding process for precision-cast blades is formulated, and a robotic grinding system is constructed. An optimal model to match the blade measurement data to the design model is then established, and a corresponding optimal matching matrix is solved to determine a position reference for precision-cast blades. Further, the theoretical cutter contacts are extracted, and the machining allowance of the precision-cast blade profile is measured. An estimation model of grinding material removal (MR) of precision-cast blades based on a neural network algorithm is established, and the correctness of the model is verified. Finally, an adaptive grinding experiment is performed on the concave surface (CC) of a precision-cast blade to verify the accuracy of the proposed grinding method. The experimental results show that, after adaptive grinding, the machining allowance of CC is distributed in the range of − 0.05 mm to 0.05 mm, which meets the machining precision requirements of the blade.
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The authors of the paper express their deep gratitude to them for the financial support of research projects.
This research is supported by the National Key R&D Program of China (Grant No. 2019YFB1703700), the Technology R&D and Application Demonstration Program of Chongqing (Grant No. cstc2018jszx-cyzdX0061), and the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJZD-M201801101).
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Zhang, M., Chen, T., Tan, Y. et al. An adaptive grinding method for precision-cast blades with geometric deviation. Int J Adv Manuf Technol 108, 2349–2365 (2020). https://doi.org/10.1007/s00170-020-05520-6
- Precision-cast blade
- Adaptive grinding
- Robotic grinding system
- Neural network
- Material removal model