Ingenieurwissenschaften und zugeordnete Tätigkeiten
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- 2019 (163) (entfernen)
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- Additive Manufacturing (32)
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Eingeladener Vortrag
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Additive Manufacturing (AM) in terms of laser powder-bed fusion (L-PBF) offers new prospects regarding the design of parts and enables therefore the production of lattice structures. These lattice structures shall be implemented in various industrial applications (e.g. gas turbines) for reasons of material savings or cooling
channels. However, internal defects, residual stress, and structural deviations from the nominal geometry are unavoidable.
In this work, the structural integrity of lattice structures manufactured by means of L-PBF was non-destructively investigated on a multiscale approach.
A workflow for quantitative 3D powder analysis in terms of particle size, particle shape, particle porosity, inter-particle distance and packing density was established.
Synchrotron computed tomography (CT) was used to correlate the packing
density with the particle size and particle shape. It was also observed that at least about 50% of the powder porosity was released during production of the struts.
Struts are the component of lattice structures and were investigated by means of laboratory CT. The focus was on the influence of the build angle on part porosity and surface quality. The surface topography analysis was advanced by the quantitative characterisation of re-entrant surface features. This characterisation was compared with conventional surface parameters showing their complementary
information, but also the need for AM specific surface parameters.
The mechanical behaviour of the lattice structure was investigated with in-situ CT under compression and successive digital volume correlation (DVC). The Deformation was found to be knot-dominated, and therefore the lattice folds unit cell layer wise.
The residual stress was determined experimentally for the first time in such lattice structures. Neutron diffraction was used for the non-destructive 3D stress investigation. The principal stress directions and values were determined in dependence of the number of measured directions. While a significant uni-axial stress state was found in the strut, a more hydrostatic stress state was found in the knot. In both cases, strut and knot, seven directions were at least needed to
find reliable principal stress directions.
µCT is used to validate the capability of online monitoring for in-situ detection of defects during the L-PBF build process, which is a focus of the TF project ProMoAM.
Our first experiments show that online monitoring using thermography and optical tomography cameras are able to detect defects in the built part. But further research is needed to understand root cause of the correlation.
The Measurement- and Model-based Structural Analysis (MeMoS) integrates a finite element model into least squares adjustment and thus allows to evaluate a mechanical model and measurements in a combined analysis. To examine the capability to detect and localise damage using this integrated analysis MeMoS, a small-scale truss bridge made of aluminium profiles is built as a test specimen for this purpose.
The Influence of the Temperature Gradient on the Distribution of Residual Stresses in AM AISI 316L
(2019)
Steep temperature gradients and solidification shrinkage are the main contributors to the formation of residual stresses in additively manufactured metallic parts produced by laser beam melting. The aim of this work was to determine the influence of the temperature gradient.
Diffraction results show a similar pattern for both specimens, indicating the shrinkage to be more dominant for the distribution of residual stresses than the temperature gradient. Thermography results imply that a higher energy input result in higher compressive residual stresses in the bulk.
The introduction of the 5G technology and automotive radar applications moving into higher frequency ranges trigger further miniaturization of LTCC technology (low temperature co-fired ceramics). To assess dimensional tolerances of inner metal structures of an industrially produced LTCC multilayer, computer tomography (CT) scans were evaluated by machine learning segmentation.
The tested multilayer consists of several layers of a glass ceramic substrate with low resistance silver-based vertical interconnect access (VIA). The VIAs are punched into the LTCC green tape and then filled with silver-based pastes before stacking and sintering. These geometries must abide by strict tolerance requirements to ensure the high frequency properties.
This poster presents a method to extract shape and size specific data from these VIAs. For this purpose, 4 measurements, each containing 3 to 4 samples, were segmented using the trainable WEKA segmentation, a non-commercial machine learning tool. The dimensional stability of the VIA can be evaluated regarding the edge-displacement as well as the cross-sectional area. Deviation from the ideal tubular shape is best measured by aspect ratio of each individual layer. The herein described method allows for a fast and semi-automatic analysis of considerable amount of structural data. This data can then be quantified by shape descriptors to illustrate 3-dimensional information in a concise manner. Inter alia, a 45 % periodical change of cross-sectional area is demonstrated.
This talk presents a new application for high-spatial resolution eddy current testing (ET) with magneto resistive (MR) sensor arrays. With rising popularity and availability of additive manufacturing (AM), companies mainly in the aerospace sector, set high requirements on quality control of AM parts, especially produced with selective laser melting (SLM). Since it was shown that those parts are prawn to flaws like pores or cracks, every part needs to be tested. Therefore, NDT Methods, like ET, could help to characterize SLM parts. Research on ET has shown, that offline ET with high spatial resolution MR sensor arrays is possible and that flaws as small as 50 µm could be detected while significantly reducing testing time. In this talk a first approach on automated online ET method for testing SLM parts is proposed. An approach with hundreds of MR sensor is made to maintain spatial resolution and short testing times. Classic signal conditioning methods are used to reduce cost and complexity while maintaining high testing bandwidths. The proposed idea enables further research on automated generation of testing reports, process control or automated flaw curing.