Research on Machining Errors Control by Adaptive CNC Machining Process for Near-Net-Shaped Jet Engine Blades

This study proposes an adaptive CNC machining process based on on-machine measurement to control the machining error of near-net-shaped blades. The multi-source and multi-process machining error transmission model of a near-net-shaped blade is established, and the reduction effect of the machining error transmission chain by the adaptive CNC machining process is qualitatively analyzed based on the machining error transmission flow model. The effects of the adaptive CNC machining process on the positioning benchmark error, machining position error, and machining contouring error are explored based on an experiment for the adaptive CNC machining process. In particular, the ability of the adaptive CNC machining process to cooperatively control the blade position error and the contouring error is discussed in relation to the stiffness of the blade-fixture system. The results show that the adaptive CNC machining process can reasonably reduce the machining errors caused by the positioning benchmark. The final deviation band of the blade body is reduced by 60% based on this adaptive CNC machining process. The adaptive CNC machining process can optimize the contouring error and the position error of the blade tenon root with only the stiffness of the blade-fixture system prerequisite being ensured. The adaptive CNC machining process has the excellent ability to control machining errors to improve the machining quality of the blade.

A Knowledge-Driven Approach for 3D High Temporal-Spatial Measurement of an Arbitrary Contouring Error of CNC Machine Tools Using Monocular Vision

Periodic health checks of contouring errors under unloaded conditions are critical for machine performance evaluation and value-added manufacturing. Aiming at breaking the dimension, range and speed measurement limitations of the existing devices, a cost-effective knowledge-driven approach for detecting error motions of arbitrary paths using a single camera is proposed. In combination with the PNP algorithm, the three-dimensional (3D) evaluation of large-scale contouring error in relatively high feed rate conditions can be deduced from a priori geometrical knowledge. The innovations of this paper focus on improving the accuracy, efficiency and ability of the vision measurement. Firstly, a camera calibration method considering distortion partition of the depth-of-field (DOF) is presented to give an accurate description of the distortion behavior in the entire photography domain. Then, to maximize the utilization of the decimal involved in the feature encoding, new high-efficient encoding markers are designed on a cooperative target to characterize motion information of the machine. Accordingly, in the image processing, markers are automatically identified and located by the proposed decoding method based on finding the optimal start bit. Finally, with the selected imaging parameters and the precalibrated position of each marker, the 3D measurement of large-scale contouring error under relatively high dynamic conditions can be realized by comparing the curve that is measured by PNP algorithm with the nominal one. Both detection and verification experiments are conducted for two types of paths (i.e., planar and spatial trajectory), and experimental results validate the measurement accuracy and advantages of the proposed method.