Analytische Chemie
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The influence of the support structure on residual stress and distortion in SLM Inconel 718 parts
(2017)
The effect of support structure and of removal from the base plate on the residual stress state in Selective Laser Melted IN718 parts was studied by means of synchrotron X-ray diffraction. The residual stresses in subsurface region of two elongated prisms in as-built condition and after the cutting from the base plate were determined. One sample was directly built on a base plate and another one on a support structure. Also, the distortion on the top surface due to stress release was measured by contact profilometry. High tensile residual stress values were found, with pronounced stress gradient along the hatching direction. In the sample on support stress redistribution took place after removal of the base plate, as opposed to simple stress relaxation for the sample without support. The sample on support structure showed larger distortion compared to sample without support. We conclude that the use of a support decreases stress values but stress relieving heat treatments are still needed.
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.