Angewandte Physik
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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.
Copper sulfide shows some unique physico-chemical properties that make it appealing as a cathode active material (CAM) for solid-state batteries (SSBs). The most peculiar feature of the electrode reaction is the reversible formation of μm-sized Cu crystals during cycling, despite its large theoretical volume change (75%). Here, the dynamic microstructural evolution of CuS cathodes in SSBs is studied using in situ synchrotron X-ray tomography. The formation of μm-sized Cu within the CAM particles can be clearly followed. This process is accompanied by crack formation that can be prevented by increasing the stack pressure from 26 to 40 MPa. Both the Cu inclusions and cracks show a preferential orientation perpendicular to the cell stack pressure, which can be a result of a z-oriented expansion of the CAM particles during lithiation. In addition, cycling leads to a z-oriented reversible displacement of the cathode
pellet, which is linked to the plating/stripping of the Li counter electrode. The pronounced structural changes cause pressure changes of up to 6 MPa within the cell, as determined by operando stack pressure measurements. Reasons for the reversibility of the electrode reaction are discussed and are attributed to the favorable combination of soft materials.