5 Werkstofftechnik
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Knowledge representation in the materials science and engineering (MSE) domain is a vast and multi-faceted challenge: Overlap, ambiguity, and inconsistency in terminology are common. Invariant and variant knowledge are difficult to align cross-domain. Generic top-level semantic terminology often is too abstract, while MSE domain terminology often is too specific.
The PMDco is designed in direct support of the FAIR principles to address immediate needs of the global experts community and their requirements. The illustrated findings show how the PMDco bridges semantic gaps between high-level, MSE-specific, and other science domain semantics, how the PMDco lowers development and integration thresholds, and how to fuel it from real-world data sources ranging from manually conducted experiments and simulations as well as continuously automated industrial applications.
This presentation provides a comprehensive overview of recent developments and the current status within the Semantic Interoperability work area, with a particular emphasis on the advancements related to the Platform MaterialDigital Core Ontology (PMDco). The presentation will delve into the collaborative and community-supported curation process that has been instrumental in shaping PMDco. Additionally, we will introduce the innovative Ontology Playground, showcasing its role in fostering experimentation and exploration within the realm of ontology development.
Many metallic materials gain better mechanical properties through controlled heat treatments. For example, in age-hardenable aluminium alloys, the strengthening mechanism is based on the controlled formation of nanometre-sized precipitates, which represent obstacles to dislocation movement and consequently increase the strength. Precise tuning of the material microstructure is thus crucial for optimal mechanical behaviour under service condition of a component. Therefore, analysis of the microstructure, especially the precipitates, is essential to determine the optimum parameters for the interplay of material and heat treatment. Transmission electron microscopy (TEM) is utilized to identify precipitate types and orientations in the first step. Dark-field imaging (DF-TEM) is often used to image the precipitates and thereafter quantify their relevant dimensions. Often, these evaluations are still performed by manual image analysis, which is very time-consuming and to some extent also poses reproducibility problems.
Our work aims at a semantic representation of an automatable digital approach for this material specific characterization method under adaption of FAIR data practices. Based on DF-TEM images of different precipitation states of a wrought aluminium alloy, the modularizable, digital workflow of quantitative analysis of precipitate dimensions is described. The integration of this workflow into a data pipeline concept will also be discussed. Using ontologies, the raw image data, their respective contextual information, and the resulting output data of the quantitative image analysis can be linked in a triplestore. Publishing the digital workflow and the ontologies will ensure data reproducibility. In addition, the semantic structure enables data sharing and reuse for other applications and purposes, demonstrating interoperability.