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In this work, Pb(Zr,Ti)O3 thin films were used to fabricate well-ordered nanodot arrays by means of nanosphere lithography. This technique is based on a two-step etch process that enables excellent control of the fabrication of ordered nanodisc arrays of defined height, diameter, and pitch. Piezoresponse force microscopy was used to investigate both non-patterned and patterned films. The topography and both the out-of-plane and the in-plane polarization were deduced in this mode. Grains of nanodots with a low aspect ratio form domain structures comparable to domains in non-patterned two-dimensional films. In contrast, nanodots with a higher aspect ratio form particular structures like bi-sectioned domain assemblies, c-shaped domains or multi-domains surrounding a center domain. The patterning of the ferroelectric material was shown to affect the formation of ferroelectric domains. The initial polycrystalline films with random polarization orientation re-orient upon patterning and then show domain structures dependent on the nanodisc diameter and aspect ratio.
In this work, reactive magnetron-sputtered Pb(Zr,Ti)O3 thin films were used to fabricate well-ordered nanodot arrays by means of nanosphere lithography (NSL). NSL is based on a two-step etch process by means of, firstly adjusting the diameter of polystyrene spheres in the self-assembled polymeric nanosphere mask using reactive ion etching, and secondly transferring the mask to the substrate by ion milling with adjusted heights. Hence, structures with different aspect ratios can be fabricated.
Piezoresponse force microscopy was used as the inspection tool on both non-patterned and patterned films. Both the topography and polarization out of plane and in plane was deduced in this mode. Grains of nanodots with low aspect ratio form domain structures comparable to domains in non-patterned films. In contrast to that, nanodots with a higher aspect ratio form particular structures. The in-plane amplitude images show mostly a bisectioned domain assembly, while the out-of-plane amplitude images show in some cases more complex structures like 'c'-shaped domains or multi-domains around a center domain.
The patterning of the ferroelectric material was shown to affect the formation of ferroelectric domains. The initial polycrystalline, randomly-ordered films are re-oriented and show domain structures depending on nanodisc diameter and aspect ratio. This may enable tailoring of ferroelectric materials in their piezoelectric and pyroelectric properties by patterning.
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.