An important part in the efficient and robust design of turbine blades is to capture the details of any manufacturing uncertainty. However, the data available detailing the manufacturing uncertainty inevitably contains variability due to inherent errors in any measurement process. The presented work proposes a methodology that employs existing probabilistic data analysis techniques, namely, Principal Component Analysis (PCA), Multivariate Analysis of Variance (MANOVA) and Fast Fourier Transform (FFT) analysis for separation of the measurement error from measurement data to obtain the underlying manufacturing uncertainty. This manufacturing uncertainty is further segregated in terms of the manufacturing uncertainty with time and the blade to blade manufacturing error. A method for dimensionality reduction is employed which utilizes prior information available on the variance of the measurement error for each measurement location. The application of the proposed methodology leads to reconstruction of new datasets that may be used for generating 3-d models of the manufactured blade shapes. These 3-d models may then be used further for Finite Element Analysis (FEA) in standard FEA tools.

This content is only available via PDF.
You do not currently have access to this content.