Here you will find overview publications relevant to the MaterialDigital platform (PMD) in which PMD employees were involved. For even more in-depth research results, please visit our Forum.

Semantic Representation of Low-Cycle Fatigue Testing Data Using a Fatigue Test Ontology and ckan.kupferdigital Data Management System

Hossein Beygi Nasrabadi, Thomas Hanke, Birgit Skrotzki


Addressing a strategy for publishing open and digital research data, this paper presents the approach for streamlining and automating the process of storage and conversion of research data to those of semantically queryable data on the web. As the use case for demonstrating and evaluating the digitalization process, the primary datasets from Low-Cycle Fatigue (LCF) testing of several copper alloys are prepared. The Fatigue Test Ontology (FTO) and ckan.kupferdigital data management system are developed as two main prerequisites of the data digitalization process. FTO has been modeled according to the content of the fatigue testing standard and by reusing the Basic Formal Ontology (BFO), Industrial Ontology Foundry (IOF) core ontology, and Material Science and Engineering Ontology (MSEO). The ckan.kupferdigital data management system was also constructed in such a way that enables the users to prepare the protocols for mapping the datasets into the knowledge graph, and automatically convert all the primary datasets to those machine-readable data which are represented by the Web Ontology Language (OWL). The retrievability of the converted digital data was also evaluated by querying the example competency questions, confirming that ckan.kupferdigital enables publishing open data that can be highly reused in the semantic web.

A novel digitalization approach for smart materials – ontology-based access to data and models

Jürgen Maas, Mena Leemhuis, Jana Mertens, Hedda Schmidtke, Robert Courant, Martin Dahlmann, Sebastian Stark, Andrea Böhm, Kenny Pagel, Maximilian Hinze, Daniel Pinkal, Michael Wegener, Martin Wagner, Thomas Sattel, Holger Neubert, Özgür Özçep


Smart materials react to physical fields (e.g. electric, magnetic and thermal fields) and can be used as sensors, actuators and generators due to their bidirectional behavior. Easy and multiscale access to material data and models enables efficient research and development with regard to the selection of appropriate materials and their optimization towards specific applications. However, different working principles, measurement and analysis methods, as well as data storage approaches lead to heterogeneous and partly inconsistent datasets. The ontology-based data access (OBDA) is a suitable method to access such heterogeneous datasets easily and quickly, while material models can transform material data across certain scales for different applications. In order to connect both capabilities, we present an extended approach enabling an ontology-based data and model access (OBDMA), also supporting FAIR (Findable, Accessible, Interoperable, and Re-usable). The OBDMA system comprises four main levels, the query, the ontology, the mapping and the database. Storing knowledge at these different levels increases the interchangeability and enables variable datasets, which is essential, especially for dynamic research fields such as smart materials. In our paper, the principles and advantages of the OBDMA approach are demonstrated for different subclasses of smart materials, but can be transferred to other materials, too.

FAIR and Structured Data: A Domain Ontology Aligned with Standard-Compliant Tensile Testing

Markus Schilling, Bernd Bayerlein, Philipp von Hartrott, Jörg Waitelonis, Henk Birkholz, Pedro Dolabella Portella, Birgit Skrotzki


The digitalization of materials science and engineering (MSE) is currently leading to remarkable advancements in materials research, design, and optimization, fueled by computer-driven simulations, artificial intelligence, and machine learning. While these developments promise to accelerate materials innovation, challenges in quality assurance, data interoperability, and data management have to be addressed. In response, the adoption of semantic web technologies has emerged as a powerful solution in MSE. Ontologies provide structured and machine-actionable knowledge representations that enable data integration, harmonization, and improved research collaboration. This study focuses on the tensile test ontology (TTO), which semantically represents the mechanical tensile test method and is developed within the project Plattform MaterialDigital (PMD) in connection with the PMD Core Ontology. Based on ISO 6892-1, the test standard-compliant TTO offers a structured vocabulary for tensile test data, ensuring data interoperability, transparency, and reproducibility. By categorizing measurement data and metadata, it facilitates comprehensive data analysis, interpretation, and systematic search in databases. The path from developing an ontology in accordance with an associated test standard, converting selected tensile test data into the interoperable resource description framework format, up to connecting the ontology and data is presented. Such a semantic connection using a data mapping procedure leads to an enhanced ability of querying. The TTO provides a valuable resource for materials researchers and engineers, promoting data and metadata standardization and sharing. Its usage ensures the generation of finable, accessible, interoperable, and reusable data while maintaining both human and machine actionability.

From concrete mixture to structural design - a holistic optimization procedure in the presence of uncertainties

Atul Agrawal, Erik Tamsen, Phaedon-Stelios Koutsourelakis, Jörg F. Unger


Designing civil structures such as bridges, dams or buildings is a complex task requiring many syn- ergies from several experts. Each is responsible for different parts of the process. This is often done in a sequential manner, e.g. the structural engineer makes a design under the assumption of certain material properties (e.g. the strength class of the concrete), and then the material engineer optimizes the material with these restrictions. This paper proposes a holistic optimization procedure, which combines the concrete mixture design and structural simulations in a joint, forward workflow that we ultimately seek to invert. In this manner, new mixtures beyond standard ranges can be considered. Any design effort should account for the presence of uncertainties which can be aleatoric or epis- temic as when data is used to calibrate physical models or identify models that fill missing links in the workflow. Inverting the causal relations established poses several challenges especially when these involve physics-based models which most often than not do not provide derivatives/sensitivities or when design constraints are present. To this end, we advocate Variational Optimization, with pro- posed extensions and appropriately chosen heuristics to overcome the aforementioned challenges. The proposed methodology is illustrated using the design of a precast concrete beam with the objective to minimize the global warming potential while satisfying a number of constraints associated with its load-bearing capacity after 28days according to the Eurocode, the demoulding time as computed by a complex nonlinear Finite Element model, and the maximum temperature during the hydration.

Data provenance - from experimental data to trustworthy simulation models and standards

Unger, F., Jörg; Robens-Rademacher, Annika; Tamsen, Erik


FAIR (findable, accessible, interoperable and reusable) data usage is one of the main principals that many of the research and funding organizations include in their strategic plans, which means that following the main principals of FAIR data is required in many research projects. The definition of data being FAIR is very general. When implementing that for a specific application or project or even setting a standardized procedure within a working group, a company or a research community, many challenges arise. In this contribution, an overview about our experience with different methods and tools is outlined.

We begin with a motivation on potential use cases for the application of FAIR data with increasing complexity starting from a reproducible research paper over collaborative projects with multiple participants such as Round-Robin tests up to data-based models within standardization codes, applications in machine learning or parameter estimation of physics-based simulation models.

In a second part, different options for structuring the data (including metadata schema) are discussed. The first one is the openBIS system, which is an open-source lab notebook and PostgreSQL based data management system. A second option is a semantic representation using RDF based on ontologies for the domain of interest.

In a third section, requirements for workflow tools to automate data processing are discussed and their integration into reproducible data analysis is presented with an outlook on required information to be stored as metadata in the database.

Finally, the presented procedures are exemplarily demonstrated for the calibration of a temperature dependent constitutive model for additively manufactured mortar. A metadata schema for a rheological measurement setup is derived and implemented in an openBIS database. After a short review of a potential numerical model predicting the structural build-up behavior, the automatic workflow to use the stored data for model parameter estimation is demonstrated.

Insights from an OTTR-centric Ontology Engineering Methodology

Moritz Blum, Basil Ell, Philipp Cimiano


OTTR is a language for representing ontology modeling patterns, which enables to build ontologies or knowledge bases by instantiating templates. Thereby, particularities of the ontological representation language are hidden from the domain experts, and it enables ontology engineers to, to some extent, separate the processes of deciding about what information to model from deciding about how to model the information, e.g., which design patterns to use. Certain decisions can thus be postponed for the benefit of focusing on one of these processes. To date, only few works on ontology engineering where ontology templates are applied are described in the literature. In this paper, we outline our methodology and report findings from our ontology engineering activities in the domain of Material Science. In these activities, OTTR templates play a key role. Our ontology engineering process is bottom-up, as we begin modeling activities from existing data that is then, via templates, fed into a knowledge graph, and it is top-down, as we first focus on which data to model and postpone the decision of how to model the data. We find, among other things, that OTTR templates are especially useful as a means of communication with domain experts. Furthermore, we find that because OTTR templates encapsulate modeling decisions, the engineering process becomes flexible, meaning that design decisions can be changed at little cost.

Toward a digital materials mechanical testing lab

Hossein Beygi Nasrabadi, Thomas Hanke, Matthias Weber, Miriam Eisenbart, Felix Bauer, Roy Meissner, Gordian Dziwis, Ladji Tikana, Yue Chen, Birgit Skrotzki


To accelerate the growth of Industry 4.0 technologies, the digitalization of mechanical testing laboratories as one of the main data-driven units of materials processing industries is introduced in this paper. The digital lab infrastructure consists of highly detailed and standard-compliant materials testing knowledge graphs for a wide range of mechanical testing processes, as well as some tools that enable the efficient ontology development and conversion of heterogeneous materials’ mechanical testing data to the machine-readable data of uniform and standardized structures. As a basis for designing such a digital lab, the mechanical testing ontology (MTO) was developed based on the ISO 23718 and ISO/IEC 21838-2 standards for the semantic representation of the mechanical testing experiments, quantities, artifacts, and report data. The trial digitalization of materials mechanical testing lab was successfully performed by utilizing the developed tools and knowledge graph of processes for converting the various experimental test data of heterogeneous structures, languages, and formats to standardized Resource Description Framework (RDF) data formats. The concepts of data storage and data sharing in data spaces were also introduced and SPARQL queries were utilized to evaluate how the introduced approach can result in the data retrieval and response to the competency questions. The proposed digital materials mechanical testing lab approach allows the industries to access lots of trustworthy and traceable mechanical testing data of other academic and industrial organizations, and subsequently organize various data-driven research for their faster and cheaper product development leading to a higher performance of products in engineering and ecological aspects.

The Intersection Between Semantic Web and Materials Science

Andre Valdestilhas, Bernd Bayerlein, Benjamin Moreno Torres, Ghezal Ahmad Jan Zia, Thilo Muth


The application and benefits of Semantic Web Technologies (SWT) for managing, sharing, and (re-)using of research data are demonstrated in implementations in the field of Materials Science and Engineering (MSE). However, a compilation and classification are needed to fully recognize the scattered published works with its unique added values. Here, the primary use of SWT at the interface with MSE is identified using specifically created categories. This overview highlights promising opportunities for the application of SWT to MSE, such as enhancing the quality of experimental processes, enriching data with contextual information in knowledge graphs, or using ontologies to perform specific queries on semantically structured data. While interdisciplinary work between the two fields is still in its early stages, a great need is identified to facilitate access for nonexperts and develop and provide user-friendly tools and workflows. The full potential of SWT can best be achieved in the long term by the broad acceptance and active participation of the MSE community. In perspective, these technological solutions will advance the field of MSE by making data FAIR. Data-driven approaches will benefit from these data structures and their connections to catalyze knowledge generation in MSE.

Digitalized data access of DE material models and their parameters using an OBD(M)A approach

Jana Mertens, Mena Leemhuis, Özgür Özçep, Hedda Schmidtke, Jürgen Maas


Dielectric Elastomer (DE) transducers are characterized by their geometrical dimensions and in particular by the properties of the elastomer and electrode materials. Therefore, in addition to dimensions, it is advantageous to consider optimization of material properties to fulfill transducer requirements, such as blocking force, free stroke, or response time. A big challenge in describing the properties of DE materials deals with utilizing different but commonly used hyperelastic material models and their parameters, which differ in complexity and corresponding model errors. Thus, determined material parameters are not necessarily consistent. In addition, parameters are depending on the measurement method, its conditions and the samples themselves. All of this leads to heterogeneous datasets making data access more complicated and in certain cases impossible for users. To overcome this, OBDA (ontology-based data access) approaches have been proven to access these heterogeneous datasets individually and efficiently and to gain the relevant information with the help of an ontology. Within a research project funded by the Federal Ministry of Education and Research, an extended OBDA approach is developed: OBDMA (ontology-based data and model access) combines data access with model-based working steps. While the joint project considers four different smart material classes, this paper focuses on dielectric materials and their transducers, in particular the development of methods to handle hyperelastic material models and their parameters. The various possibilities of material models and parameter identification methods are discussed on the basis of a measurement curve. Finally, the working principle and the advantages of the OBDMA system are demonstrated by means of a representative DE use case.

Ontopanel: A Tool for Domain Experts Facilitating Visual Ontology Development and Mapping for FAIR Data Sharing in Materials Testing

Yue Chen, Markus Schilling, Philipp von Hartrott, Hossein Beygi Nasrabadi, Birgit Skrotzki & Jürgen Olbricht


In recent years, the design and development of materials are strongly interconnected with the development of digital technologies. In this respect, efficient data management is the building block of material digitization and, in the field of materials science and engineering (MSE), effective solutions for data standardization and sharing of different digital resources are needed. Therefore, ontologies are applied that represent a map of MSE concepts and relationships between them. Among different ontology development approaches, graphical editing based on standard conceptual modeling languages is increasingly used due to its intuitiveness and simplicity. This approach is also adopted by the Materials-open-Laboratory project (Mat-o-Lab), which aims to develop domain ontologies and method graphs in accordance with testing standards in the field of MSE. To suit the actual demands of domain experts in the project, Ontopanel was created as a plugin for the popular open-source graphical editor to enable graphical ontology editing. It includes a set of pipeline tools to foster ontology development in, comprising imports and reusage of ontologies, converting diagrams to Web Ontology Language (OWL), verifying diagrams using OWL rules, and mapping data. It reduces learning costs by eliminating the need for domain experts to switch between various tools. Brinell hardness testing is chosen in this study as a use case to demonstrate the utilization of Ontopanel.

Toward a Li-Ion Battery Ontology Covering Production and Material Structure

Marcel Mutz, Milena Perovic, Philip Gümbel, Veit Steinbauer, Andriy Taranovskyy, Yunjie Li, Lisa Beran, Tobias Käfer, Klaus Dröder, Volker Knoblauch, Arno Kwade, Volker Presser, Dirk Werth, Tobias Kraus (MaterialDigital project: DigiBatMat)


An ontology for the structured storage, retrieval, and analysis of data on lithium-ion battery materials and electrode-to-cell production is presented. It provides a logical structure that is mapped onto a digital architecture and used to visualize, correlate, and make predictions in battery production, research, and development. Materials and processes are specified using a predetermined terminology; a chain of unit processes (steps) connects raw materials and products (items) of battery cell production. The ontology enables the attachment of analytical methods (characterization methods) to items. Workshops and interviews with experts in battery materials and production processes are conducted to ensure that the structure is conformable both for industrial-scale and laboratory-scale data generation and implementation. Raw materials and intermediate products are identified and defined for all steps to the final battery cell. Steps and items are defined based on current standard materials and process chains using terms that are in common use. Alternative structures and the connection of the ontology to other existing ontologies are discussed. The contribution provides a pragmatic, accessible way to unify the storage of materials-oriented lithium-ion battery production data. It aids the linkage of such data with domain knowledge and the automation of data analysis in production and research.

Generating FAIR research data in experimental tribology

Nikolay T. Garabedian, Paul J. Schreiber, Nico Brandt, Philipp Zschumme, Ines L. Blatter, Antje Dollmann, Christian Haug, Daniel Kümmel, Yulong Li, Franziska Meyer, Carina E. Morstein, Julia S. Rau, Manfred Weber, Johannes Schneider, Peter Gumbsch, Michael Selzer & Christian Greiner


Solutions for the generation of FAIR (Findable, Accessible, Interoperable, and Reusable) data and metadata in experimental tribology are currently lacking. Nonetheless, FAIR data production is a promising path for implementing scalable data science techniques in tribology, which can lead to a deeper understanding of the phenomena that govern friction and wear. Missing community-wide data standards, and the reliance on custom workflows and equipment are some of the main challenges when it comes to adopting FAIR data practices. This paper, first, outlines a sample framework for scalable generation of FAIR data, and second, delivers a showcase FAIR data package for a pin-on-disk tribological experiment. The resulting curated data, consisting of 2,008 key-value pairs and 1,696 logical axioms, is the result of (1) the close collaboration with developers of a virtual research environment, (2) crowd-sourced controlled vocabulary, (3) ontology building, and (4) numerous – seemingly – small-scale digital tools. Thereby, this paper demonstrates a collection of scalable non-intrusive techniques that extend the life, reliability, and reusability of experimental tribological data beyond typical publication practices.

SimStack: An Intuitive Workflow Framework

Celso R. C. Rêgo, Jörg Schaarschmidt, Tobias Schlöder, Montserrat Penaloza-Amion, Saientan Bag, Tobias Neumann, Timo Strunk and Wolfgang Wenzel


Establishing a fundamental understanding of the nature of materials via computational simulation approaches requires knowledge from different areas, including physics, materials science, chemistry, mechanical engineering, mathematics, and computer science. Accurate modeling of the characteristics of a particular system usually involves multiple scales and therefore requires the combination of methods from various fields into custom-tailored simulation workflows. The typical approach to developing patch-work solutions on a case-to-case basis requires extensive expertise in scripting, command-line execution, and knowledge of all methods and tools involved for data preparation, data transfer between modules, module execution, and analysis. Therefore multiscale simulations involving state-of-the-art methods suffer from limited scalability, reproducibility, and flexibility. In this work, we present the workflow framework SimStack that enables rapid prototyping of simulation workflows involving modules from various sources. In this platform, multiscale- and multimodule workflows for execution on remote computational resources are crafted via drag and drop, minimizing the required expertise and effort for workflow setup. By hiding the complexity of high-performance computations on remote resources and maximizing reproducibility, SimStack enables users from academia and industry to combine cutting-edge models into custom-tailored, scalable simulation solutions.

Experimental characterization and numerical analysis of additively manufactured mild steel under monotonic loading conditions

J. Lizarazu, L. Göbel, S. Linne, S. Kleemann, T. Lahmer, Ch. Rößler & J. Hildebrand


Additive Manufacturing (AM), for the case of metals, is a technology developed to create 3D products by following a layer-by-layer welding procedure. In this work, the tensile behavior of wire arc additively manufactured mild steel is studied experimentally and numerically. The microstructure of the metal is strongly influenced by the AM process that involves several heating and cooling cycles; therefore, it is first analyzed with optical microscopy, scanning electron microscopy, energy-dispersive X-ray spectroscopy and X-ray diffraction to identify the different phases and to extract the grain properties. With this information, two approaches are used to build the Representative Volume Element, which will be part of a multi-scale material model. The first approach constitutes a synthetic generation of grains according to a Voronoi Tessellation and the second one an image-based representation. Afterwards, a virtual tensile test for the determination of the stress–strain relation of the material is performed, which is later compared with the measurements of a real tensile test carried out on several specimens that were obtained using the wire arc additive manufacturing technique.

It can be observed that the influence of the welding direction on the stiffness and the ductility of the additively manufactured steel product is rather low, yielding similar results in both parallel and perpendicular directions. Additionally, a softening behavior of the material is noticed.