Correcting Hardening Artifacts of Aero-Engine Blades with an Iterative Linear Fitting Technique Framework.

Sensors (Basel)

School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China.

Published: March 2024

AI Article Synopsis

  • Aero engines are vital for aerospace vehicles, with cermet turbine blades enhancing their performance.
  • This study addresses the challenge of beam hardening artifacts in X-ray imaging of cermet blades by introducing a hardening-correction framework based on iterative linear fitting.
  • Results demonstrate that this new method significantly improves image quality metrics, indicating its potential for advancing imaging techniques in the evaluation of next-generation aero-engine turbine blades.

Article Abstract

Aero engines are the key power source for aerospace vehicles. Cermet turbine blades are the guarantee for the new-generation fighters to improve aero-engine overall performance. X-ray non-destructive reconstruction can obtain the internal structure and morphology of cermet turbine blades. However, the beam hardening effect causes artifacts in objects and affects the reconstruction quality, which is an issue that needs to be solved urgently. This study proposes a hardening-correction framework for industrial computed tomography (ICT) images based on iterative linear fitting. First, an iterative binarization was performed to improve the penetration length accuracy of the forward projection. Then, the proposed linear fitting technology combined with the Hermite function model is derived and analyzed to obtain suitable parameters of blade data. Finally, the fitting curves of the blade data, using the proposed method and the traditional polynomial fitting method, were analyzed and compared and were used to correct the engine turbine blade projection data to reconstruct different groups of tomographic images. Different groups of tomographic images were analyzed using three quantitative image quality evaluation indicators. The results show that the root-mean-square error (RMSE) of the tomographic image obtained by the proposed framework is 0.0133, which is lower than that of the compared method. The peak signal-to-noise ratio (PSNR) is 37.7050 dB and the feature structural similarity (FSIM) is 0.9881, which are both higher than that of the compared method. The proposed method improves the hardening-artifact-correction capability and can obtain higher-quality images, which provides new ideas for the development of imaging and detection of new-generation aero-engine turbine blades.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10975699PMC
http://dx.doi.org/10.3390/s24062001DOI Listing

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