Machine learning in industrial X-ray computed tomography – a review

Bellens, Simon; Guerrero, Patricio; Vandewalle, Patrick; Dewulf, Wim

doi:10.1016/j.cirpj.2024.05.004

CIRP Journal of Manufacturing Science and Technology

2024

DOI: 10.1016/j.cirpj.2024.05.004

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Machine learning in industrial X-ray computed tomography – a review

Simon Bellens,

Patricio Guerrero,

Patrick Vandewalle

et al.

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A New Methodology for Selecting CT Scanning Parameters Depending on the Density of Materials

Ostrowska,

Sładek,

Wołkanowski

et al. 2024

Materials

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The CT (computed tomography) scanner has been used for many years now not only for medical measurements but also in many industries, for example, in defectoscopy for measuring sheet thickness and checking the joining of materials, as well as for measuring the geometry of individual components. This type of scanner is a good complement to coordinate contact and non-contact measurements for intra-structural measurements and inaccessible places. The variety of materials, however, makes it very difficult to select individual CT parameters. In this paper, a curve for selecting the maximum and minimum voltage of the lamp depending on the density of a given material is determined and an interpolation polynomial (1d with a third-degree polynomial) is used, by defining third-degree glued functions (cubic spline) to determine intermediate voltage values to a given material density, so as to determine full data ranges. This approach can facilitate the work of selecting scanning parameters for non-destructive testing, as this is a difficult process and sometimes consumes half of the measurement time. The practical experiments were carried out at the Accredited Coordinate Metrology Laboratory to develop a multi-criteria matrix for selecting CT measurement parameters for measurement accuracy. This approach reduced the time by an average of half an hour and effectively optimized the selection of scanning parameters.

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A New Methodology for Selecting CT Scanning Parameters Depending on the Density of Materials

Ostrowska,

Sładek,

Wołkanowski

et al. 2024

Materials

View full text Add to dashboard Cite

show abstract

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Machine learning in industrial X-ray computed tomography – a review

Cited by 1 publication

References 106 publications

A New Methodology for Selecting CT Scanning Parameters Depending on the Density of Materials

A New Methodology for Selecting CT Scanning Parameters Depending on the Density of Materials

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