Design of an experimental approach based on the contrast-to-noise ratio measurement for X-ray computed tomography parameters optimization applied to a carbon fiber-reinforced polymer materials scan

Authors

  • Sara Casaccia Università Politecnica delle Marche http://orcid.org/0000-0001-9909-0816
  • Giuseppe Pandarese università politecnica delle Marche
  • Vincenzo Castorani HP composites
  • Gian Marco Revel università politecnica delle Marche

DOI:

https://doi.org/10.21014/actaimeko.v13i3.1789

Keywords:

X-ray computed tomography, scanning parameters optimization, CFRP materials, CCD model

Abstract

This paper proposes a systematic approach for the optimization of scan parameters for industrial X-ray computed tomography (XCT), as regard its specific application as diagnostic tool on carbon fiber-reinforced polymer materials (CFRP).

This procedure allows the system operator to overcome suboptimal scan results due to a subjective choice of XCT parameters. In this work, XCT scan quality has been measured in terms of contrast-to-noise ratio (CNR) metric, by calculating it on collected XCT 2D projection images. A four-factor five-level central composite design (CCD) was implemented to perform experiments, and a quadratic polynomial model was chosen to describe the effects of XCT scanning parameters combination on CNR measurement and finally to predict optimal results. Analysis of variance was carried out to evaluate the significance of the model on the response, reporting a R2 of 97.1%, and response surface analyses were also performed for CNR optimization purposes. In order to validate the CCD results, different XCT parameters combinations, coming from the CCD analysis on projection images, were used to run different scans, and, as result, the optimal CNR predicted from the model was also reflected in an optimal CNR measured on the reconstructed XCT images.

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Published

2024-09-06

Issue

Vol. 13 No. 3 (2024)

Section

Research Papers

License

Copyright (c) 2024 Sara Casaccia, Giuseppe Pandarese, Vincenzo Castorani, Gian Marco Revel

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