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Towards Interoperability: Digital Representation of a Material Specific Characterization Method
(2022)
Certain metallic materials gain better mechanical properties through controlled heat treatments. For example, in age-hardenable aluminum alloys, the strengthening mechanism is based on the controlled formation of nanometer sized precipitates, which represent obstacles to dislocation movement. Precise tuning of the material structure is critical for optimal mechanical behavior in the application. Therefore, analysis of the microstructure and especially the precipitates is essential to determine the ideal parameters for the interplay of material and heat treatment. Transmission electron microscopy (TEM) is utilized to identify precipitate types and orientations in a first step. Dark-field imaging (DF-TEM) is often used to image the precipitates and to quantify their relevant dimensions.
The present work aims at the digital representation of this material-specific characterization method. Instead of a time-consuming, manual image analysis, an automatable, digital approach is demonstrated. Based on DF-TEM images of different precipitation states of a wrought aluminum alloy, a modularizable digital workflow for quantitative precipitation analysis is presented. The integration of this workflow into a data pipeline concept will also be discussed. Thus, by using ontologies, the raw image data, their respective contextual information, and the resulting output data from the quantitative precipitation analysis can be linked in a triplestore. Publishing the digital workflow and the ontologies will ensure the reproducibility of the data. In addition, the semantic structure enables data sharing and reuse for other applications and purposes, demonstrating interoperability.
The presented work is part of two digitization initiatives, the Platform MaterialDigital (PMD, materialdigital.de) and Materials-open-Laboratory (Mat-o-Lab).
Towards interoperability: Digital representation of a material specific characterization method
(2023)
Certain metallic materials gain better mechanical properties through controlled heat treatments. In age-hardenable aluminum alloys, the strengthening mechanism is based on the controlled formation of nanometer sized precipitates, which hinder dislocation movement. Analysis of the microstructure and especially the precipitates by transmission electron microscopy allows identification of precipitate types and orientations. Dark-field imaging is often used to image the precipitates and quantify their relevant dimensions.
The present work aims at the digital representation of this material-specific characterization method. Instead of a time-consuming, manual image analysis, a digital approach is demonstrated. The integration of an exemplary digital workflow for quantitative precipitation analysis into a data pipeline concept is presented. Here ontologies enable linking of contextual information to the resulting output data in a triplestore. Publishing digital workflow and ontologies ensures the reproducibility of the data. The semantic structure enables data sharing and reuse for other applications and purposes, demonstrating interoperability.
The PMD Core Ontology (PMDco) is a comprehensive set of building blocks produced via consensus building. The ontological building blocks provide a framework representing knowledge about fundamental concepts used in Materials Science and Engineering (MSE) today. The PMDco is a mid-level ontology that establishes connections between narrower MSE application ontologies and domain neutral concepts used in already established broader (top-level) ontologies. The primary goal of the PMDco design is to enable interoperability between various other MSE-related ontologies and other common ontologies.
PMDco’s class structure is both comprehensive and extensible, rendering it an efficient tool to structure MSE knowledge. The PMDco serves as a semantic middle-layer unifying common MSE concepts via semantic mapping to other semantic representations using well-known key terms used in the MSE domain. The PMDco enables straight-forward documentation and tracking of science data generation and in consequence enables high-quality FAIR data that allows for precise reproducibility of scientific experiments.
The design of PMDco is based on the W3C Provenance Ontology (PROV-O), which provides a standard framework for capturing the production, derivation, and attribution of resources. Via this foundation, the PMDco enables the integration of data from various data origins and the representation of complex workflows.
In summary, the PMDco is a valuable advancement for researchers and practitioners in MSE domains. It provides a common MSE vocabulary to represent and share knowledge, allowing for efficient collaboration and promoting interoperability between diverse domains. Its design allows for the systematic integration of data and metadata, enabling seamless tracing of science data. Overall, the PMDco is a crucial step towards a unified and comprehensive understanding of the MSE domain in general.
A large amount of publicly available data is reproduced every day in the field of materials science, while these kind of material data can have different formats and types like paper-type publications, standards, datasheets or isolated datasets in repositories. However, gathering a specific library from such extensive and diverse material data is always challenging for the materials scientists and engineers, since the time-related limitations are not allowed to fully access the large publicly available databases; search across these disparate databases, manage the large volumes of heterogeneous datasets, and integrate data from multiple sources. To address these challenges and make data findable, accessible, interoperable, and reusable (FAIR), an efficient data management system is necessary to build comprehensive, documented, and connected data spaces in the future. A formal standardized knowledge representation through an ontology can address such problems and make data more available and interoperable between related domains. Ontology can also rich machine processable semantic descriptions that increases the performance of scientific searches. In this regard, the Platform MaterialDigital (PMD) is currently working on developing a high-level ontology for the materials and material related processes. For example, in one of the PMD projects of “KupferDigital”, we will try to develop a data ecosystem for digital materials research based on ontology-based digital representations of copper and copper alloys. As a case study, this paper describes the methodology for ontology development of Brinell hardness, based on PMD core ontology. The methodology we describe includes the following steps; gathering the required domain terminology from different resources like standards (DIN EN ISO 6506-1) and test reports, representing the performance of a standard-conformant hardness test and the treatment of the recorded values up to a “reportable” hardness value for a material, designing the process chain according to the semantic technologies, and developing a domain-level ontology of Brinell hardness based on PMD ontology semantic formalization. Apart the mentioned methodology, some interesting tools and methods were introduced and ontology design challenges and possible solutions for modelling materials and processes were discussed. Furthermore, a dataset from the Brinell hardness measurement of cast copper samples is prepared for testing the query process.
The materials mechanical testing ontology (MTO) was developed by collecting the mechanical testing vocabulary from ISO 23718 standard, as well as the standardized testing processes described for various mechanical testing of materials like tensile testing, Brinell hardness test, Vickers hardness test, stress relaxation test, and fatigue testing. Confirming the ISO/IEC 21838-2 standard, MTO utilizes the Basic Formal Ontology (BFO), Common Core Ontology (CCO), Industrial Ontologies Foundry (IOF), Quantities, Units, Dimensions, and data Types ontologies (QUDT), and Material Science and Engineering Ontology (MSEO) as the upper-level ontologies. Reusing these upper-level ontologies and materials testing standards not only makes MTO highly interoperable with other ontologies but also ensures its acceptance and applicability in the industry. MTO represents the mechanical testing entities in the 230 classes and four main parts: i) Mechanical testing experiments entities like tensile, hardness, creep, and fatigue tests as the subclasses of mseo:Experiment, ii) Mechanical testing quantity concepts such as toughness, elongation, and fatigue strength in the appropriate hierarchies of bfo:Disposition and bfo:Quality classes, iii) Mechanical testing artifacts like indenter as the subclasses of cco:Artifact, and iv) mechanical testing data like the stress-strain, S-N, or creep curves as the subclasses of cco:InformationContentEntity. MTO is publicly available via the KupferDigital GitLab repository.
This study aims to represent an approach for transferring the materials testing datasets to the digital schema that meets the prerequisites of the semantic web. As a use case, the tensile stress relaxation testing method was evaluated and the testing datasets for several copper alloys were prepared. The tensile stress relaxation testing ontology (TSRTO) was modeled following the test standard requirements and by utilizing the appropriate upper-level ontologies. Eventually, mapping the testing datasets into the knowledge graph and converting the data-mapped graphs to the machine-readable Resource Description Framework (RDF) schema led to the preparation of the digital version of testing data which can be efficiently queried on the web.
This research deals with the development of the Vickers hardness knowledge graph, mapping the example dataset in them, and exporting the data-mapped knowledge graph as a machine-readable Resource Description Framework (RDF). Modeling the knowledge graph according to the standardized test procedure and using the appropriate upper-level ontologies were taken into consideration to develop the highly standardized, incorporable, and industrial applicable models. Furthermore, the Ontopanel approach was utilized for mapping the real experimental data in the developed knowledge graphs and the resulting RDF files were successfully evaluated through the SPARQL queries.
This research deals with the development of the Vickers hardness knowledge graph, mapping the example dataset in them, and exporting the data-mapped knowledge graph as a machine-readable Resource Description Framework (RDF). Modeling the knowledge graph according to the standardized test procedure and using the appropriate upper-level ontologies were taken into consideration to develop the highly standardized, incorporable, and industrial applicable models. Furthermore, the Ontopanel approach was utilized for mapping the real experimental data in the developed knowledge graphs and the resulting RDF files were successfully evaluated through the SPARQL queries.
Mechanical testing ontology
(2023)
The materials mechanical testing ontology (MTO) was developed by collecting the mechanical testing vocabulary from ISO 23718 standard, as well as the standardized testing processes described for various mechanical testing of materials like tensile testing, Brinell hardness test, Vickers hardness test, stress relaxation test, and fatigue testing. Confirming the ISO/IEC 21838-2 standard, MTO utilizes the Basic Formal Ontology (BFO), Common Core Ontology (CCO), Industrial Ontologies Foundry (IOF), Quantities, Units, Dimensions, and data Types ontologies (QUDT), and Material Science and Engineering Ontology (MSEO) as the upper-level ontologies.
Ontologies that represent a map of the concepts and relationships between them, are becoming an effective solution for data standardization and integration of different resources in the field of materials science, as efficient data storage and management is the building block of material digitization. However, building a domain ontology is not a simple task. It requires not only a collaborative effort between ontologists and domain experts, but also the modeling approaches and tools play a key role in the process. Among all approaches, graphical representation of domain ontologies based on standard conceptual modeling languages is widely used because of its intuitiveness and simplicity. Various tools have been developed to realize this approach in an intuitive way, such as Protégé plugins and web visualization tools.
The Materials-open-Lab (MatOLab) project, which aims to develop ontologies and workflows in accordance with testing standards for the materials science and engineering domains, adopted a UML (Unified Modeling Language) approach based on the diagrams.net. It is a powerful, popular, open-source graphical editor. In practical case studies, however, many users’ needs could not be met, such as reusing ontology, conversion, and data mapping. Users must switch between different tools to achieve a certain step, and thereby invariably increase learning cost. The lack of validation also leads to incorrect diagrams and results for users who are not familiar with the ontology rules. To address these issues, we designed Ontopanel, a diagrams.net-based plugin that includes a set of pipeline tools for semantic modeling: importing and displaying protégé-like ontologies, converting diagrams to OWL, validating diagrams by OWL rules, and mapping data. It uses diagrams.net as the front-end for method modeling and Django as the back-end for data processing. As a web-based tool, it is very easy to expand its functionality to meet changing practical needs.