Characterization of Filigree Additively Manufactured NiTi Structures Using Micro Tomography and Micromechanical Testing for Metamaterial Material Models.

This study focuses on the influence of additive manufacturing process strategies on the specimen geometry, porosity, microstructure and mechanical properties as well as their impacts on the design of metamaterials. Filigree additively manufactured NiTi specimens with diameters between 180 and 350 µm and a nominal composition of Ni50.9Ti49.1 (at %) were processed by laser powder bed fusion in a first step. Secondly, they structures were characterized by optical and electron microscopy as well as micro tomography to investigate the interrelations between the process parameters, specimen diameters and microstructure. Each specimen was finally tested in a micro tensile machine to acquire the mechanical performance. The process strategy had, besides the resulting specimen diameter, an impact on the microstructure (grain size) without negatively influencing its quality (porosity). All specimens revealed a superelastic response while the critical martensitic phase transition stress decreased with the applied vector length. As a conclusion, and since the design of programmable metamaterials relies on the accuracy of FEM simulations, precise and resource-efficient testing of filigree and complex structures remains an important part of creating a new type of metamaterials with locally adjusted material behavior.

Automated Quantitative Analyses of Fatigue-Induced Surface Damage by Deep Learning.

The digitization of materials is the prerequisite for accelerating product development. However, technologically, this is only beneficial when reliability is maintained. This requires comprehension of the microstructure-driven fatigue damage mechanisms across scales. A substantial fraction of the lifetime for high performance materials is attributed to surface damage accumulation at the microstructural scale (e.g., extrusions and micro crack formation). Although, its modeling is impeded by a lack of comprehensive understanding of the related mechanisms. This makes statistical validation at the same scale by micromechanical experimentation a fundamental requirement. Hence, a large quantity of processed experimental data, which can only be acquired by automated experiments and data analyses, is crucial. Surface damage evolution is often accessed by imaging and subsequent image post-processing. In this work, we evaluated deep learning (DL) methodologies for semantic segmentation and different image processing approaches for quantitative slip trace characterization. Due to limited annotated data, a U-Net architecture was utilized. Three data sets of damage locations observed in scanning electron microscope (SEM) images of ferritic steel, martensitic steel and copper specimens were prepared. In order to allow the developed models to cope with material-specific damage morphology and imaging-induced variance, a customized augmentation pipeline for the input images was developed. Material domain generalizability of ferritic steel and conjunct material trained models were tested successfully. Multiple image processing routines to detect slip trace orientation (STO) from the DL segmented extrusion areas were implemented and assessed. In conclusion, generalization to multiple materials has been achieved for the DL methodology, suggesting that extending it well beyond fatigue damage is feasible.

Mass spectrometric studies of benzoxazine resorcarenes.

Eleven differently substituted 3,4-dihydro-2H-1,3-benzoxazine resorcarenes were studied by electrospray ionisation (ESI) and matrix-assisted laser desorption/ionisation (MALDI) mass spectrometry, using Fourier transform ion cyclotron resonance (FT-ICR) and time-of-flight (TOF) mass spectrometers, respectively. Under ESI conditions it was possible to transfer the intact resorcarenes from solution to the gas phase, yielding [M + H](+) and [M + 2H](+) ions as the main ions observed. Energy increase of the ions induced ready decomposition through successive eliminations of four CH(2)NR groups. Ion-molecule reactions showed that the ionising proton was situated somewhere inside the molecule and could not be reached with neutral reagent gases. In the host-guest complexation experiments, the benzoxazine resorcarenes studied turned out to be poor hosts for alkali metal and ammonium ions. In MALDI experiments, 2,5-dihydroxybenzoic acid proved to be the best matrix for these compounds. However, the intensity of the [M + H](+) ions was low for all compounds, and extensive fragmentation with consecutive elimination of CH(2)NR groups was observed.