Exploratory factor analysis for machining error data of compressor blades based on dimensionality-reduced statistical analysis method

Abstract

Abstract Various machining errors inevitably occur on aero-engine compressor blades, including leading-edge contour error, trailing-edge contour error, camber contour error, and more. The current complexity surrounding the numerous ma-chining error types and their obscure interrelationships imposes immense effort for aerodynamic analysis and hin-ders overall error control. Thus, elucidating error correlations to achieve error dimensionality reduction is imperative. This study pioneers a dimensionality reduction approach via factor analysis to conduct a comprehensive statistical analysis of 13 types of blade machining errors. The proposed technique can categorize the 13 errors into three groups, each dominated by a distinct common factor. To validate the accuracy of the extracted factors, the factor scores of the three identified latent factors are computed. Cluster analysis is then performed on the factor scores, and the clustering results are visualized by t-Distributed Stochastic Neighbor Embedding (t-SNE). Furthermore, boot-strap resampling establishes the 95% confidence intervals for the factor scores. Capitalizing on the grouping struc-ture uncovered by factor analysis, multiple linear regression models are built for the errors within each group, and then, a preliminary conjecture is made about the potential key error types for each group of errors based on the re-gression coefficients. This hypothesis is then evidenced by the statistical analysis of cross-section profile error data of 28 blades. The present work can not only optimize machining processes but also relax tolerance requirements and diminish the effort of aerodynamic analysis.