Influence of CT image processing on the predicted impact of pores on fatigue of additively manufactured Ti6Al4V and AlSi10Mg. Issue 3 (11th July 2022)

• Record Type: Journal Article
• Authors: Gebhardt, Ulrike; Schulz, Paul; Raßloff, Alexander; Koch, Ilja; Gude, Maik; Kästner, Markus
• Other Names: Hartmann Stefan guestEditor.; Diebels Stefan guestEditor.
• Is Part Of: Mitteilungen der Gesellschaft für Angewandte Mathematik und Mechanik. Volume 45:Issue 3/4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-07-11
• Subjects: additive manufacturing -- computed tomography -- image processing -- pore analysis; Mathematics -- Periodicals; Mechanics, Applied -- Periodicals; 510.5
• DOI: 10.1002/gamm.202200017
• Languages: English
• ISSNs: 0936-7195

Abstract

Abstract: Pores are inherent to additively manufactured components and critical especially in technical components. Since they reduce the component's fatigue life, a reliable identification and description of pores is vital to ensure the component's performance. X‐ray computed tomography (CT) is an established and non‐destructive testing method to investigate internal defects. The CT scan process can induce noise and artefacts in the resulting images which afterwards have to be reduced through image processing. To reconstruct the internal defects of a component, the images need to be segmented in defect region and bulk material by applying a threshold. The application of the threshold as well as the previous image processing alter the geometry and size of the identified defects. This contribution aims to quantify the influence of selected commercial image processing and segmentation methods on identified pores in several additively manufactured components made of AlSi10Mg and Ti6Al4V as well as in an artificial CT scan. To that aim, gray value histograms and characteristic parameters thereof are compared for different image processing tools. After the segmentation of the processed images, particle characteristics are compared. The influence of image processing and segmentation on the predicted fatigue life of the material is evaluated through the change of the largest pore in each set of data applying Murakami's empirical area $$ \sqrt{\mathrm{area}} $$ ‐parameter model.