An optimization framework for enhancing profile accuracy in robotic grinding of compressor blade edge

摘要

The machining precision of blades is critical to the service performance of aero engines. The Leading Edge (LE) of high-pressure compressor blades poses a challenge for precision machining due to its thin size, high degree of bending, and significant change of curvature. Aimed at optimizing the machining error, this paper presents a framework that integrates toolpath planning and process parameter regulation. Firstly, an Iterative Subdivision Algorithm (ISA) for clamped B-spline curve is proposed, based on which toolpath planning method towards the LE is developed. Secondly, the removal effect of Cutter Contact (CC) point on the sampling points is investigated in the calculation of grinding dwell time by traversing in u - v space. A global material removal model is constructed for the solution. Thirdly, the previous two steps are interconnected based on the Improved Whale Optimization Algorithm (IWOA), and the optimal parameter combination is searched using the Root Mean Square Error (RMSE) of the machining error as the objective function. Based on this, the off-line programming and robotic grinding experiments are carried out. The experimental results show that the proposed method with error optimization can achieve 0.0143 mm mean value and 0.0160 mm standard deviations of LE surface error, which is an improvement of 32.5% and 33.9%, respectively, compared with previous method.