Blades are a core component of aero-engines. The accuracy of the blade profile of an aero-engine is crucial for its normal operation. Considering the limitations of low-overlap scanning point cloud data stitching in blade profile measurements, this paper proposes an improved feature fusion-trimmed iterative closest-point (TrICP) algorithm, realizing automatic stitching of three-dimensional (3D) point clouds scanned by laser measurements. In the stitching experiment of the Stanford 3D scan dataset Dragon-scan point cloud, the success rates of viewing angle differences of 24° and 48° were 100% and 66.7%, respectively, which were higher than those obtained using the TrICP algorithm, FPFH+SAC-IA, and ISS_BR+SHOT. The proposed algorithm exhibited high stitching success rates and efficiencies in the point cloud stitching experiment with large transformations. Moreover, the algorithm was employed as a prestitching tool. An automatic stitching method was further proposed by combining the point-to-plane iterative closest-point algorithm for performing precise stitching and the pose-map optimization algorithm for performing automatic stitching experiments on blade laser measurement data. The point cloud data measured using a coordinate-measuring machine further verified the stitching accuracy of our algorithm. The automatic stitching method exhibited good performance with regard to the scanning point cloud data of turbine rotor and guide blades (turbine rotor and guide blades have different shapes). The root-mean-square errors of the stitching experiments were 0.0354 and 0.0398 mm, meeting the error requirement of blade design and processing. Results show that the proposed algorithm is superior to traditional algorithms and shows promise for engineering applications.

In femtosecond laser helical drilling, the laser focus moves downward into the workpiece as material is removed, which is one of the main features used to improve the processing efficiency. Numerical modeling is the primary method used to study this process. To reduce the spatial scale of the numerical model, the influence of the helical trajectory was not considered in this study. Instead, the influences of the pulse interval and the downward velocity of the laser focus on the ablation process during deep-hole processing with multi-pulse femtosecond laser ablation were explored, and the existing two-temperature model was adapted. We used the critical-point phase separation model to describe the material ablation process with a femtosecond laser. Using a copper workpiece, we simulated an ablation process in which multiple pulses from a femtosecond laser were focused onto the workpiece and the focus moved into the workpiece at a constant speed. We used the finite element method to determine the variation in the electron and lattice temperatures, as well as the ablation depth under different pulse intervals with the focus moving downward at different velocities. The results demonstrate that the pulse interval is an important factor affecting the ablation depth during multi-pulse femtosecond laser ablation. As the pulse interval increased, the ablation depth first increased and then decreased. The laser ablation efficiency was highest when the pulse interval was 200 ps. The downward velocity of the laser focus determined the defocusing distance during laser processing. We obtained a high processing efficiency when a reasonable downward velocity was adopted to maintain the defocusing distance within approximately 50 nm.