Optimized detection of lack-of-fusion defects in 3D printed components using µCT

Abstract

A robust detection of defects and a reliable quality assurance is of great importance for the application of additive manufactured components, especially in heavily regulated industries such as the medical. Several techniques are currently used together for quality control and mostly reference parts are printed alongside for a subsequent destructive testing. Oftentimes, this elaborate quality assurance is hampering the widespread use of this particularly energy and resource-saving technology.

By using micro computed tomography (µCT) scans, even complex components, such as individualized implants can be examined directly for defects. Typically, there are three types of defects in a powder bed fusion process, of which gas porosities and inclusions can be easily identified by means of µCT. Lack-of-fusion pores, on the other hand, which are caused by insufficient layer bonding in the printing process, are difficult to identify depending on the direction and their contrast to the solid material.

As part of the publicly funded research project ENABL3D, this study examines methods for the optimized detection of these lack-of-fusion defects. For this purpose, different filtering and phase retrieval algorithms applied to projection and volume data in order to reduce noise, separate signal information and optimize the contrast of defects. Additionally, different reconstruction techniques and hardware setups are evaluated.

The defects detected in this way are compared to the actual defects using microscopic cross-sectional images (micrographs) of test specimens. Thus, the potential of the evaluated algorithms is objectively compared and their parameters are optimized. It will be shown how phase contrast and other filtering methods have improved the detection of lack-of-fusion in additively manufactured metal components.