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.

Uniaxial fatigue testing of micro-mechanical metallic specimens can provide valuable insight into damage formation. Magnetic and piezomagnetic testing are commonly used for qualitative characterization of damage in ferromagnetic specimens. Sensitive and accurate measurements with magnetic sensors is a key part of such a characterization. This work presents an experimental setup to induce structural defects in a micro-mechanical fatigue test. Simultaneously, the resulting piezomagnetic signals are measured during the complete lifetime of the tested specimen. The key component is a highly sensitive optically pumped magnetometer (OPM) used to measure the piezomagnetic hysteresis of a small specimen whose structural defects can be analyzed on a small scale by other metallographic characterization methods as well. This setup aims to quantify the magnetic signatures of damage during the fatigue process, which could enable non-destructive mechanical testing of materials. This paper reports the initial results obtained from this novel micro-magneto-mechanical test setup for a ferritic steel specimen.

Micro electro mechanical systems, coatings, and thin films contain materials that are often subjected to complex loading conditions and high frequencies. Therefore, the implementation of a resonant multiaxial fatigue setup for small-scale samples is of high interest. Finite Element simulations have been used to design small-scale samples which possess very close bending and torsional resonant frequencies. The bending and torsional modes are excited by two piezo actuators working either in or out of phase with one another. The bending and torsional amplitudes are measured independently by a laser and the actuator amplitudes are controlled by a Field Programmable Gate Array. The fatigue setup can be used with a varying range of sample sizes from centimeters down to tens of micrometers. The novel multiaxial resonant micro fatigue setup as well as results (fatigue damage for Ni and lifetime for Cu) from bending small-scale fatigue tests are shown and discussed.

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 Ni_50.9Ti_49.1 (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.