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Accurate Reconstruction of Gas Turbine Blade Geometry Using 3D/2D Rigid Registration and CT View Optimization — arXiv:2609.01368

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.01368
License (as archived): CC0-1.0
Archived: 2026-10-17T07:13:54.245Z
SHA-256 of the archived text: 52c4d8202f2d20dc7bdca502d18665a1445f954fd997639177c88814467da9d3
Stable URL of this page: https://metraipolis.com/documento/dc_aed3733ddf37

How to cite

Accurate Reconstruction of Gas Turbine Blade Geometry Using 3D/2D Rigid Registration and CT View Optimization — arXiv:2609.01368 — arXiv (en), https://arxiv.org/abs/2609.01368 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_aed3733ddf37 (Metraipolis archive dc_aed3733ddf37, sha256 52c4d8202f2d20dc7bdca502d18665a1445f954fd997639177c88814467da9d3)

BibTeX

@misc{dcaed3733ddf37,
  title = {Accurate Reconstruction of Gas Turbine Blade Geometry Using 3D/2D Rigid Registration and CT View Optimization — arXiv:2609.01368},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.01368},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_aed3733ddf37; sha256 52c4d8202f2d20dc7bdca502d18665a1445f954fd997639177c88814467da9d3},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.01368. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Non-destructive X-ray and computed tomography (CT) testing are essential for ensuring the dimensional accuracy of manufactured components with complex internal structures, such as the cooling channels in gas turbine blades, which directly affect thermal performance and service life. This study presents a multipart 3D-2D rigid registration approach for aligning CAD models with X-ray projections as an alternative to CT reconstruction for part inspection and measurement. A greedy registration algorithm sequentially aligns the blade's exterior before registering its internal components by maximizing the mutual information between simulated and acquired X-ray images. This stepwise approach reduces problem complexity and improves alignment accuracy. View angles are optimized using a greedy method that iteratively selects angles to minimize dimensional measurement errors. The results indicate that a small number of oblique views provides the best accuracy, although a broad range of angles yields acceptable results. The method achieves subpixel registration accuracy, with errors below one-fifth of the magnified detector-pixel pitch. Image noise and defects reduce registration precision, but direct registration in projection space mitigates these effects compared with CT reconstruction. Appropriate view selection can therefore preserve acceptable subpixel accuracy in the presence of image noise and defects.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-02 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.01368 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T07:00:58.982Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).