Abstract
Metal additive manufacturing (MAM) has found emerging application in the aerospace, biomedical and defence industries. However, the lack of reproducibility and quality issues are regarded as the two main drawbacks to AM. Both of these aspects are affected by the distribution of defects (e.g. pores) in the AM part. Computed tomography (CT) allows the determination of defect sizes, shapes and locations, which are all important aspects for the mechanical properties of the final part. In this paper, data-constrained modelling (DCM) with multi-energy synchrotron X-rays is employed to characterise the distribution of defects in 316L stainless steel specimens manufactured with laser metal deposition (LMD). It is shown that DCM offers a more reliable method to the determination of defect levels when compared to traditional segmentation techniques through the calculation of multiple volume fractions inside a voxel, i.e. by providing sub-voxel information. The results indicate that the samples are dominated by a high number of small light constituents (including pores) that would not be detected under the voxel size in the majority of studies reported in the literature using conventional thresholding methods.
Highlights
Additive manufacturing (AM) is defined as “a process of joining materials to make objects from 3D model data, usually layer upon layer, as opposed to subtractive manufacturing methodologies” (ASTM F2792-12a)
For sample 1 (Fig. 2a), Computed tomography (CT) suggests grain growth in the build direction with the light constituents mainly distributed along the grain boundaries
The presence of elongated grains roughly parallel to the build direction is a result of the steep temperature gradient created by the directed local heat source [2], i.e. the heat conduction in the build direction is typically higher than in the build plane [1, 5]
Summary
There are a number of challenges for AM to establish itself as a core technology. In this context, quality and repeatability are regarded as the two main drawbacks of AM [15, 17,18,19,20]. Some issues are yet to be addressed in order to improve the potential of computed tomography in detecting defects in metal AM: (1) the limited resolution of the scans, (2) lack of thresholding robustness (i.e. different results due to the effects of colour thresholding and CT artefacts and noise), (3) incapability of differentiating between pores and inclusions for conventional segmentation techniques. DCM (data-constrained modelling), on the other hand, enables more accurate determination of the composition of a sample, providing more detailed information and allowing to calculate materials distributions below CT resolution, i.e. inside a voxel [50, 51, 53].
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