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We use pattern matching of experimental and dynamically simulated backscattered Kikuchi diffraction (BKD) patterns to increase the orientation precision of electron backscatter diffraction (EBSD) measurements. In order to quantify the improvement in orientation precision, we analyze the experimental distribution of the kernel average misorientation (KAM) angles. We find that for the same raw data, i.e. the same EBSD data acquisition time budget, the pattern matching approach improves the KAM resolution by an order of magnitude compared to orientation data delivered from the conventional Hough-transform based data analysis. This quantitative improvement enables us to interpret small orientation changes in plastically deformed materials which are hidden in noisy orientation data delivered from the reference EBSD system.
As an application example, we analyze a ferritic-martensitic steel (P92) sample before and after low-cycle fatigue (LCF) loading (±0.3% strain) at 620 °C. Whereas the low precision of the EBSD orientation data from the manufacturer software does not allow a reliable discrimination of the gradually changing microstructure, we find very clear systematic differences of the local microstructure after the pattern matching orientation refinement of the initial, raw pattern data. For the investigated P92 sample, the KAM-angle histograms are well described by two log-normal distributions indicating the already tempered and the remaining and mostly untempered martensite.
The amount of data generated worldwide is constantly increasing. These data come from a wide variety of sources and systems, are processed differently, have a multitude of formats, and are stored in an untraceable and unstructured manner, predominantly in natural language in data silos. This problem can be equally applied to the heterogeneous research data from materials science and engineering. In this domain, ways and solutions are increasingly being generated to smartly link material data together with their contextual information in a uniform and well-structured manner on platforms, thus making them discoverable, retrievable, and reusable for research and industry. Ontologies play a key role in this context. They enable the sustainable representation of expert knowledge and the semantically structured filling of databases with computer-processable data triples.
In this perspective article, we present the project initiative Materials-open-Laboratory (Mat-o-Lab) that aims to provide a collaborative environment for domain experts to digitize their research results and processes and make them fit for data-driven materials research and development. The overarching challenge is to generate connection points to further link data from other domains to harness the promised potential of big materials data and harvest new knowledge.