Abstract
Precision forged blades are increasingly adopted in the field of aero-engine to save material quantity and improve machining efficiency. Edge shapes with sharply changed curvature of the precision forged blade are hard to be reconstructed when removing redundant blank material left by forging process. To address this problem, an original approach is proposed to predict and reconstruct an expected profile of the edge shape in adaptive machining. In this approach, the expected profile is obtained by reconstructing an axial curve and attaching blade thickness to this axial curve. In the first stage, the axial curve is first calculated point by point with the Newton-Raphson method. To predict the absent axial curve without measured data, the endpoint of the calculated axial curve is found according to an established deviation trend. Then, the absent axial curve is predicted as a spline curve with the minimum strain energy by solving an optimization problem. The expected profile is then reconstructed and smoothed in the second stage. In this stage, the blade thickness is calculated to establish a deformation trend of the forged blade. According to this deformation trend, the thickness of the absent part is then predicted and attached to the reconstructed axial curve to form an expected profile. After that, this expected profile is smoothed by solving a multi-objective optimization problem to satisfy industrial requirements and conditions. This approach is finally verified with a precision forged blade and compared with a referenced method. The results show that the proposed method can effectively reconstruct a qualified profile curve in machining a precision forged blade.
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Funding
This work was supported by the National Science and Technology Major Project on CNC Machine tool, China (no. 2013ZX04011031).
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Feng, Y., Ren, J. & Liang, Y. Prediction and reconstruction of edge shape in adaptive machining of precision forged blade. Int J Adv Manuf Technol 96, 2355–2366 (2018). https://doi.org/10.1007/s00170-018-1771-x
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DOI: https://doi.org/10.1007/s00170-018-1771-x