Coaxiality prediction for aeroengines precision assembly based on geometric distribution error model and point cloud deep learning

Abstract

Assembly accuracy of aeroengines influences operation performance and service life. The coaxiality of the aeroengine is the main index of assembly accuracy and is also a core index to represent assembly quality. However, direct measurement of coaxiality is a difficult technical problem due to the sealed structure of the aeroengine casing system. A coaxiality prediction method is proposed to obtain coaxiality and assist assembly by geometric distribution error modeling and point cloud deep learning. The prediction process consists of three steps. In the beginning, the geometric distribution error model is established to construct the accurate dense point cloud of aeroengine part surfaces by the non-uniform rational B-splines (NURBS) method based on the coordinate measuring machine collecting information. Then, the mapping between the dense point cloud and coaxiality is established to obtain an assembly dataset by the virtual assembly. Finally, the dataset is fed to a new point cloud deep learning backbone, Self-channel cross attention point network, and realizes end-to-end coaxiality prediction based on the aeroengine surface point cloud. The geometric distribution error model is tested on the aeroengine simulated parts with 0.001 mm accuracy. The prediction method is verified on the aeroengine simulated parts and compared with other point cloud deep learning baselines. The method proposed in this paper realizes 93.17% prediction accuracy with 0.01 mm coaxiality precision which is a high performance and meets the requirements of industrial measurement. This paper provides an effective coaxiality prediction model for the aeroengine casing system, to improve the accuracy and efficiency of the aeroengine assembly.