Abstract
Rotor assembly is a core tache in the whole process of aero-engine manufacturing. Preventing out-of-tolerance of concentricity is one of the primary tasks. Conventional assembly approaches are based on a manual test with the dial indicator, depending on experience appraises, which lack systematic and quantitative precision design theory. As a result, two issues need to be solved: the modeling problem of complicated geometric variations in three-dimensions, as well as the abnormal distribution of ubiquitous actual deviations. This work attempts to propose a novel probabilistic approach for three-dimensional variation analysis in rotor assembly. Based on rotor’s revolving characteristics and multistage stacking process, Jacobian–Torsor model is adopted to establish the variation propagation, and Pearson distribution family is used to derive the probability density function, which can quickly determine the variation distribution pattern and efficiently perform statistical variation analysis. A real case of mechanical assemblies consisting of revolving axisymmetric components is concerned. The results show that the suggested method has a similar accuracy, but much higher efficiency than conventional methods. Calculations agree with the experimentations, and the probability distribution type of the part’s variation has an appreciable impact on the final assembly precision.
Highlights
The core of aero-engine consists of a large number of rotor parts
As this paper focuses on the general case of the mechanical assembly consisting of revolving axisymmetric components [18,19,20,21,22,23], which is widely used in the aero-engine industry, especially in the engine rotors fabricating [24, 25], there is a clear need to adopt a simple and rapid probabilistic approach for determining the likelihood that the mechanical assembly is acceptable or not, so as to consider the practical distribution of variation variables and efficiently deal with large sets of data
Probability density functions (PDF) for the eccentricity were formulized based on the assumption that the component variations were statistically independent, zero-mean Gaussian random variables with known standard deviations
Summary
The core of aero-engine consists of a large number of rotor parts. As the high-speed rotating component, aero-engine rotor is usually produced as a combination of different rotor parts, to keep its repairability and maintainability. Due to the presence of part’s manufacturing error, datum error, positioning error etc., variation accumulation is inevitable during aero-engine rotor assembly process. Minor variations are caused in the actual component from the nominal geometry [1]. As the assembling process went on, these variations would propagate and drive the final dimensions of assembly out of specification. Reducing product cost and enhancing quality is the tallest target that the enterprise pursues. Statistical variation analysis is an excellent way for predicting
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