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Revealing properties of structural materials by combining regression-based algorithms and nano indentation measurements

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.