Error distribution prediction of five-axis on-machine measurement for aerospace structural parts

Release time:2026-10-01  Hits:

• Affiliation of Author(s):机电工程学院
• Journal:Measurement
• Key Words:Five-axis on-machine measurement; Multi-source errors; Errorpropagation; Probability density function; Geometric dimensioning and tolerancing
• Abstract:On-machine measurement (OMM) using a touch-trigger probe (TTP) can identify the allowance distribution on aerospace structural parts during adaptive machining. Therefore, accurate description and prediction of OMM accuracy are essential to improve the machining accuracy of parts. However, existing methods for evaluating OMM results mostly focus on the average values while neglecting their dispersion. To address this issue, a five-axis OMM error distribution prediction method for aerospace structural parts is proposed. The expectation and standard uncertainty (EASU) are used to quantify the trueness and precision of the measurement results, respectively. Furthermore, the EASU of the machine tool (MT) positioning error, probe eccentricity error and workpiece alignment error in the measurement system are evaluated. Subsequently, the propagation model of multi-source error EASU during the measurement is derived. Notably, the propagation model addresses the correlation between the error sources and between the derived variables in the propagation process, and retains the second-order Taylor series term, thereby improving the accuracy of the prediction model under nonlinear conditions. Taking the typical geometric dimensioning and tolerancing (GD&T) on aerospace structural parts as an example, an OMM error distribution prediction model for GD&T is established. Finally, both numerical simulations and experiments were conducted on an aero-engine casing and impeller to verify the effectiveness and extensibility of the proposed method.
• Co-author:万能,王道
• First Author:kouxueqin,wangliangliang,chengbin,zhuangqixin
• Indexed by:Journal paper
• Volume:267 (2026) 120573
• Translation or Not:no
• Date of Publication:2026-01-26