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.

While the traditional trajectory planning methods are used in robotic belt grinding of blades with an uneven machining allowance distribution, it is hard to obtain the preferable profile accuracy and surface quality to meet the high-performance requirements of aero-engine. To solve this problem, a novel trajectory planning method is proposed in this paper by considering the developed interpolation algorithm and the machining allowance threshold. The residual height error obtained from grinding experiments of titanium alloy sample was compensated to modify the calculation model of row spacing, and a new geometric algorithm was presented to dynamically calculate the cutter contact points based on this revised calculation model and the dichotomy method. Subsequently, the off-line machining program is generated based on a double-vector controlling method to obtain an optimal contact posture. On this basis, three sets of robotic grinding tests of titanium alloy blades were conducted to investigate the advantages of the proposed method. The comparative experimental results revealed that the presented algorithm had improved the surface profile accuracy of blade by 34.2% and 55.1%, respectively. Moreover, the average machined surface roughness was achieved to 0.3 µm and the machining efficiency was obviously promoted. It is concluded that this research work is beneficial to comprehensively improve the machined quality of blades in robotic belt grinding.