U: Primary Shaping and Micro Structure Development
Processes and methods for the synthesis of micro samples were chosen by focusing on their microstructure. It is supposed to enable as many options as possible for following mechanical, thermal and thermos-mechanical treatments to realize a high outcome of samples within a short period of time while keeping high flexibility regarding the desired variety of alloy. The CRC envisages working on two synthesis techniques which differ fundamentally in their conceptual approach. While one of the methods is based on melt drop generation, the other one uses a solid and a laser beam to locally fuse defined layers of material. In high- throughput synthesis, both methods produce a variety of sample geometries with different microstructures for treatments. Those happen to be evaluated regarding their suitability for the method “Farbige Zustände”.
D: Descriptor Evaluation
The choice of appropriate descriptors to map the required material properties constitutes a major challenge. In the CRC, descriptors are to be developed and tested. This involves an iterative process with an initial set of descriptors during the first phase.
S: Scaling and Processes
A transfer of descriptors evaluated on micro samples to material properties requires correlations of the predictor function at certain support points. This requires micro and macro samples with a comparable microstructure which are then used to evualuate descriptors on the micro and macro scale as well as material properties such as tensile strength on the macro scale. The knowledge of those allows the definition of the predictor function by heuristic search methods.
P: Predictor Function, Planning and Controlling
The condition for a successful discovery of new structural materials is a systematical analysis of collected data, its structuring and the embedding of all results in a search algorithm. Especially a formalization and evaluation of descriptors and the resulting determination of the predictor function are of primary importance. The predictor function is determined by multi-criteria-decision-making and a system of hypothesis based on formal methods. The techniques of multi-criteria-decision-making assist finding relational operators for descriptors, which are essential for the valuation function of the search algorithm. The system of hypothesis enables a validation of if- and- then relations in the collected data. Here a domain-specific language is developed that allows experts to give complex requests to the data. A proven hypothesis strengthens the predictor function as generalization. If the hypothesis is proven false, a counterexample is provided which is additional informative. Also an analytic approach describes and inverts the data and functions.