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Data-driven methods for developing new structural materials require large databases to identify new materials from known process routes, the resulting microstructures, and their properties. Due to the high number of parameters for such process chains, this can only be achieved with methods that allow high sample throughputs. This paper presents the experimental approach of the "Farbige Zustände" method through a case study. Our approach features a high-temperature drop-on-demand droplet generator to produce spherical micro-samples, which are then heat-treated and subjected to various short-time characterizations, which yield a large number of physical, mechanical, technological, and electrochemical descriptors. In this work, we evaluate achievable throughput rates of this method resulting in material property descriptions per time unit. More than 6000 individual samples could be generated from different steels, heat-treated and characterized within 1 week. More than 90,000 descriptors were determined to specify the material profiles of the different alloys during this time. These descriptors are used to determine the material properties at macro-scale.

Within the development of a novel high-throughput method for the discovery of new structural materials, a compression test on spherical micro samples is established as a short-term characterization process. In this study, the experimental investigations regarding the elastic–plastic deformation behavior of microspheres are carried out on the bearing steel X46Cr13 in 10 different heat treatment conditions. Characteristic values, so-called descriptors, can be derived directly from continuously measured force–displacement curves or are normalized by taking the sample geometry into account. In addition to micro compression tests with quasi-static loading, load increase tests are performed, in which a spherical sample is repeatedly loaded and unloaded with progressively increasing loads. Loading and unloading phases and the development of the displacement amplitude of the hysteresis are analyzed. New descriptors of load increase tests are extracted and discussed regarding their statistical significance and validity as descriptor showing a high potential for a fast characterization of the material behavior during cyclic loading. The descriptor-based micro compression test proves to be a robust method and is a promising approach for a fast evaluation of different material conditions.

For the establishment of a novel development process of new structural materials, short-term characterization methods capable of testing hundreds of spherical micro samples are needed. This paper introduces a compression test on spherical micro samples as a short-term characterization method to investigate the elastic-plastic deformation behavior. To demonstrate the potential of this newly developed method, the micro compression test is performed with a maximum loading of 300 N on 100Cr6 (AISI 52100 bearing steel) samples, with a diameter of 0.8 mm, in 15 different heat treatment conditions. The austenitizing temperature is varied between 800 and 1150 °C. Tempering of the samples is carried out in a differential scanning calorimetry process with temperatures of 180, 230 and 300 °C. Out of force-displacement curves and stress-strain relations, so-called descriptors (characteristic values) which are sensitive to the applied heat treatment can be extracted. The change of mechanical properties due to heat treatment and the resulting microstructure is presented by the trend of a stress descriptor in dependence of austenitizing and annealing temperature, which can be compared to the trend of the tensile strength as a material property obtained by conventional tensile tests. The trend of the descriptor determined in the compression test on spherical samples indicates the validity of this approach as a short-term characterization method.

For the establishment of a novel development process of new structural materials, short-term characterization methods capable of testing hundreds of spherical micro samples are needed. This paper introduces a compression test on spherical micro samples as a short-term characterization method to investigate the elastic-plastic deformation behavior. To demonstrate the potential of this newly developed method, the micro compression test is performed with a maximum loading of 300 N on 100Cr6 (AISI 52100 bearing steel) samples, with a diameter of 0.8 mm, in 15 different heat treatment conditions. The austenitizing temperature is varied between 800 and 1150 °C. Tempering of the samples is carried out in a differential scanning calorimetry process with temperatures of 180, 230 and 300 °C. Out of force-displacement curves and stress-strain relations, so-called descriptors (characteristic values) which are sensitive to the applied heat treatment can be extracted. The change of mechanical properties due to heat treatment and the resulting microstructure is presented by the trend of a stress descriptor in dependence of austenitizing and annealing temperature, which can be compared to the trend of the tensile strength as a material property obtained by conventional tensile tests. The trend of the descriptor determined in the compression test on spherical samples indicates the validity of this approach as a short-term characterization method.

The material and its microstructure define the behaviour of a part in a deformation process. A single particle deformation process is introduced as a rapid material characterization method extending already existing approaches. Particle-oriented peening is performed with spherical micro samples of the low carbon steel 100Cr6 (AISI 52100) and the martensitic stainless steel X46Cr13 (AISI 420). Three different diameters (0.6 mm, 0.8 mm and 1.0 mm) were chosen to investigate the impact of the material and the surface to volume ratio. By processing single particles, the mechanical and geometrical properties of the particle before and after the impact can be linked to the deformation behaviour during the peening process. The elastic and plastic material properties are revealed by studying the remaining plastic deformation of the particle and the velocity reduction as a result of the impact. Instrumented universal micro hardness measurements are carried out to determine the hardness of the particles and to correlate it with the behavior of the particles during the particle-oriented peening process. The plastic deformation work as a characteristic value of micro hardness measurements of the different material states is discussed. It is conceivable that the consideration of different material behaviour related values (so-called descriptors) may replace conventional material testing in the future. Using short-term characterization methods like the particle-oriented peening a fast determination of material properties is possible.

The engineering expertise has been continuously increased within the decades such that very complex constructions are feasible, hence, high-performance structural materials are strictly required, which fulfill challenging performance profiles. Conventional material evaluation techniques have reached their performance limit such that new evolutionary approaches become increasingly important: A high-throughput screening approach has been proposed, which mainly operates on micro samples and applies multiple novel screening techniques to determine various characteristic values, which both leads to high volume of multidimensional data. This high volume allows to investigate many times more new candidates compared to conventional material development techniques. It is expected that the characteristic values reflect resulting material properties, which are not directly measurable due to chemical and physical limitations. Furthermore, the fact that no direct regularities between these characteristic values and resulting material properties are known, further increases the complexity of the data processing. This work proposes a framework, which applies a state-of-the-art big data processing technique implementing a predictive function between characteristic values (determined on micro level) and material properties on macro level. In particular, a predictive function is implemented by orchestrating a kernelbased recursive least-squares algorithm, which processes micro hardness measurement (nano indentation) of micro samples to predict properties concerning the hardness as well as the yield strength, yielding to one elementary component of the high-throughput screening approach.