Evaluation of 3D-printed parts by means of high-performance computer tomographyShow others and affiliations
2018 (English)In: Journal of laser applications, ISSN 1042-346X, E-ISSN 1938-1387, Vol. 30, no 3, article id 032307Article in journal (Refereed) Published
Abstract [en]
Conventional tactile and optical testing methods are not capable to detect complex inner geometries or complex surface shapes. Detecting porosities in parts is also not possible with those nondestructive methods. Among other material parameters, geometrical accuracy is essential to determine part's quality. Additive manufacturing processes also have to be optimized regarding geometry deviations caused by distortion or unfavorable orientation in the build chamber. For additive manufactured parts that incorporate previously mentioned features, high-performance computer tomography is the more suitable nondestructive testing method. Components of different materials such as plastics, ceramics, composites, or metals can be completely characterized. This nondestructive testing method was used for porosity analysis regarding the shape and local distribution of pores in an additive manufactured part to find correlations concerning the most suitable process conditions. The measured part data were also compared to original CAD files to determine zones of deviation and apply specific process strategies to avoid distortion. This paper discusses the results of integrating high-performance computer tomography (power: 500 W, max. part size: Ø 300 mm, 300 × 430 mm2) in a productionlike environment of additively manufactured parts for a wide range of technologies (i.e., electron beam melting and selective laser melting). I. INTRODUCTION
Place, publisher, year, edition, pages
American Institute of Physics (AIP), 2018. Vol. 30, no 3, article id 032307
National Category
Manufacturing, Surface and Joining Technology
Research subject
Manufacturing Systems Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-69849DOI: 10.2351/1.5040644ISI: 000443892000039Scopus ID: 2-s2.0-85048982727OAI: oai:DiVA.org:ltu-69849DiVA, id: diva2:1223519
Note
Validerad;2018;Nivå 2;2018-06-25 (andbra)
2018-06-252018-06-252023-09-14Bibliographically approved