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Additive manufacturing (AM) by selective laser melting (SLM) offers ample scope for producing geometrically complex parts as compared to the traditional subtractive manufacturing strategies. However, the residual stresses which develop during the process can limit the application of SLM parts because they can reduce the load bearing capacity as well as induce unwanted distortion depending on the boundary conditions specified in manufacturing. This study aims at the characterization of residual stresses in SLM parts by using different measurement techniques. The material used is the nickel based super Alloy 718. Microstructure as well as surface and bulk residual stresses were characterised. For residual stress analysis X-ray, synchrotron and neutron diffraction were applied. The results show different residual stress states dependent on the penetration depth in the sample offered by the different measurement techniques. Samples of Alloy 718 manufactured by SLM process can show high tensile residual stresses in the surface as high as the yield strength of the wrought alloy. Residual stresses in the bulk show considerably lower stress values.
The layer-by-layer Additive Manufacturing (AM) by means of Selective Laser Melting (SLM) offers many prospects regarding the design of a part used in aeroplane components and gas turbines. However, structural deviations from the nominal morphology are unavoidable. The cooling of the finished part leads to shrinkage and a high surface roughness is induced by attached powder particles affecting the part volume too.
The integrity and load-bearing capacity of a SLM produced lattice structure has been investigated by means of in-situ X-ray computed tomography during compression. The lattice structure was compressed by 10 % in height with an applied maximum force of 5 kN. Additionally, a single strut has been investigated ex-situ as a component of the lattice structure. With the higher resolution achieved on the single strut, the pore distribution (size and location) as well as the surface roughness were assessed. The particle and pore size distribution of the raw powder have been investigated, too. One of the main results coming from the in-situ analysis was that the nodes were identified as the weakest points in the lattice structure.
Additive Manufacturing (AM) through the Selective Laser Melting (SLM) route offers ample scope for producing geometrically complex parts compared to the conventional subtractive manufacturing strategies. Nevertheless, the residual stresses which develop during the fabrication can limit application of the SLM components by reducing the load bearing capacity and by inducing unwanted distortion, depending on the boundary conditions specified during manufacturing.
The present study aims at characterizing the residual stress states in the SLM parts using different diffraction methods. The material used is the nickel based superalloy Inconel 718. Microstructure as well as the surface and bulk residual stresses were characterized. For the residual stress analysis, X-ray, synchrotron and neutron diffraction methods were used. The measurements were performed at BAM, at the EDDI beamline of -BESSY II synchrotron- and the E3 line -BER II neutron reactor- of the Helmholtz-Zentrum für Materialien und Energie (HZB) Berlin.
The results reveal significant differences in the residual stress states for the different characterization techniques employed, which indicates a dependence of the residual state on the penetration depth in the sample. For the surface residual stresses, longitudinal and transverse stress components from both X-ray and synchrotron agree well and the obtained values were around the yield strength of the material. Furthermore, synchrotron mapping disclosed gradients along the width and length of the sample for the longitudinal and transverse stress components. On the other hand, lower residual stresses were found in the bulk of the material measured using neutron diffraction. The longitudinal component was tensile and decreased towards the boundary of the sample. In contrast, the normal component was nearly constant and compressive in nature. The transversal component was almost negligible. The results indicate that a stress re-distribution takes place during the deposition of the consecutive layers. Further investigations are planned to study the phenomenon in detail.
Additive Manufacturing (AM) through the Selective Laser Melting (SLM) route offers ample scope for producing geometrically complex parts compared to the conventional subtractive manufacturing strategies. Nevertheless, the residual stresses which develop during the fabrication can limit application of the SLM components by reducing the load bearing capacity and by inducing unwanted distortion, depending on the boundary conditions specified during manufacturing. The present study aims at characterizing the residual stress states in the SLM parts using different diffraction methods. The material used is the nickel based superalloy Inconel 718. Microstructure as well as the surface and bulk residual stresses were characterized.
For the residual stress analysis, X-ray, synchrotron and neutron diffraction methods were used. The measurements were performed at BAM, at the EDDI beamline of -BESSY II synchrotronand the E3 line -BER II neutron reactor- of the Helmholtz-Zentrum für Materialien und Energie (HZB) Berlin. The results reveal significant differences in the residual stress states for the different characterization techniques employed, which indicates the dependence of the residual state on the penetration depth in the sample. For the surface residual stresses, longitudinal and transverse stress components from X-ray and synchrotron agree well and the obtained values were around the yield strength of the material. Furthermore, synchrotron mapping disclosed gradients along the width and length of the sample for the longitudinal and transverse stress components. On the other hand, lower residual stresses were found in the bulk of the material measured using neutron diffraction. The longitudinal component was tensile and decreased towards the boundary of the sample. In contrast, the normal component was nearly constant and compressive in nature. The transversal component was almost negligible. The results indicate that a stress re-distribution takes place during the deposition of the consecutive layers. Further investigations are planned to study the phenomenon in detail.
Laser powder bed fusion is one of the most promising additive manufacturing techniques for printing complex-shaped metal components. However, the formation of subsurface porosity poses a significant risk to the service lifetime of the printed parts. In-situ monitoring offers the possibility to detect porosity already during manufacturing. Thereby, process feedback control or a manual process interruption to cut financial losses is enabled.
Short-wave infrared thermography can monitor the thermal history of manufactured parts which is closely connected to the probability of porosity formation. Artificial intelligence methods are increasingly used for porosity prediction from the obtained large amounts of complex monitoring data. In this study, we aim to identify the potential and the challenges of deep-learning-assisted porosity prediction based on thermographic in-situ monitoring.
Therefore, the porosity prediction task is studied in detail using an exemplary dataset from the manufacturing of two Haynes282 cuboid components. Our trained 1D convolutional neural network model shows high performance (R2 score of 0.90) for the prediction of local porosity in discrete sub-volumes with dimensions of (700 x 700 x 40) μm³.
It could be demonstrated that the regressor correctly predicts layer-wise porosity changes but presumably has limited capability to predict differences in local porosity. Furthermore, there is a need to study the significance of the used thermogram feature inputs to streamline the model and to adjust the monitoring hardware. Moreover, we identified multiple sources of data uncertainty resulting from the in-situ monitoring setup, the registration with the ground truth X-ray-computed tomography data and the used pre-processing workflow that might influence the model’s performance detrimentally.