Publications

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.

Modeling and Accessing Smart Materials with Shapes Constraint Language Integrity Constraints and Ontologies—The SmaDi Way

Özgür L. Özçep, Mena Leemhuis, Maximilian Winkler, Robert Courant, Thomas Sattel, Jürgen Maas

Abstract

Various types of semantic artifacts play a vital role in developing software systems, for example, information systems for materials scientists that adhere to the findability, accessibility, interoperability, reusability principles for digital assets. Among them, integrity constraints (ICs) are essential artifacts as they orthogonally add to the representation capabilities of ontologies a means to enforce consistency and completeness of given data. An IC language recommended by the worldwide web consortium (W3C) for use with linked open data is Shapes Constraint Language (SHACL). This article discusses the algorithm and evaluation results for a new SHACL validator developed in the context of SmaDi, a project for digitalizing smart materials associated with MaterialDigital. The new validator reduces SHACL constraints to SPARQL Protocol and RDF Query Language (SPARQL) queries, that is, queries in the W3C recommended query language over repositories represented in the standard syntax for linked open data, RDF. Hence, in contrast to off-the-shelf validators, it can be used with any SPARQL endpoint, even if there is only a (virtual) view of the RDF data. The article demonstrates the use of SHACL ICs for modeling some simple constraints over smart materials. The evaluation shows that our SHACL validator has processing times comparable to off-the-shelf SHACL validators.

Seamless Science: Lifting Experimental Mechanical Testing Lab Data to an Interoperable Semantic Representation

Markus Schilling, Sebastian Bruns, Bernd Bayerlein, Jehona Kryeziu, Jörg Schaarschmidt, Jörg Waitelonis, Pedro Dolabella Portella, Karsten Durst

Abstract

The scientific landscape is undergoing rapid transformations with the advent of the digital age which revolutionizes research methodologies. In materials science and engineering, an adoption of modern data management techniques is desirable to maximize the efficiency and accessibility of research efforts. Traditional practices in testing laboratories are usually inadequate for efficient data acquisition and utilization as they lead to local storage and difficulty in publication and correlation with other results. Electronic laboratory notebooks (ELNs) are promising prospects in this respect. Semantic concepts and ontologies enhance interoperability by standardizing experimental data representation. An in-laboratory pipeline seamlessly integrating an ELN with transformation scripts to convert experimental into interoperable data in a machine-actionable format is created in this study as a proof of concept. Tensile test results and the corresponding tensile test ontology are used exemplary. Linking ELN data to semantic concepts enriches the stored information while improving interpretability and reusability. Involving undergraduate students builds a bridge between theory and practice during their training and promotes their digital skills. This study underscores the potential of ELNs and knowledge representations as beneficial means toward improved data management practices that enhance collaborative research and education while ensuring compatibility with evolving standards and technologies.

Environmental Impact Assessment of Copper‐Alloy Production Using Process Simulation and Semantic Modeling

Mohsin Sajjad, Karl Gerald van den Boogaart, Leon Steinmeier, Ashak Mahmud Parvez

Abstract

In this research, how environmental impacts of products can be tracked through complex production networks based on semantic data and environmental impact calculations is demonstrated. It focuses on the example of secondary copper production and the subsequent production of copper alloys, namely bronze and brass. The adopted methodology and ontology can however be generalized to other products and environmental impact categories. In this study, HSC Sim and FactSage software are employed to model and simulate the copper alloy production process from copper scrap (CuScrap) and waste-printed circuit boards (WPCBs) and an ontology to represent the results for individual process parts is developed in a way that impact assessment is possible for all materials in a complex process network. Life cycle assessment (LCA), focused on greenhouse gas emissions (global warming potential [GWP]), is conducted using OpenLCA software and the ecoinvent 3.8 database to evaluate the environmental impacts comprehensively. In the GWP results, variations across all the studied cases are indicated, with bronze production generally exhibiting higher impacts, i.e., 33.46%, 32.33%, and 32.41% for Scrap, Mix, and printed circuit board cases, respectively, as compared to brass production due to the presence of tin (in bronze) which exhibits 3.7 times higher emissions than zinc (present in brass).

Developing an Ontology on Battery Production and Characterization with the Help of Key Use Cases from Battery Research

Vincent Nebel, Lisa Beran, Veit Königer, Amir Haghipour, Marcel Mutz, Andriy Taranovskyy, Dirk Werth, Volker Knoblauch, Tobias Kraus

Abstract

Materials science research faces challenges due to diverse and evolving measurements, materials, and methods. Managing research data in a way that is understandable, comparable, and reproducible is essential for high data quality, particularly for data science and machine learning applications. In Li-ion batteries research data storage concepts and structures vary widely between institutions and researchers, leading to difficulties in data comparison and understanding. To address the issue of data structuring, battery production and characterization ontology (BPCO) is developed. The ontology builds on existing ontologies like the Platform MaterialDigital core ontology and quantities, units, dimensions, and types ontology to model standard battery production processes, characterization methods, and materials. The BPCO is based on a workflow structure to be accessible to nonexperts and, unlike highly specialized existing ontologies, models the whole production process removing the need for separate data structures and enabling the identification of dependencies between parameters. This work builds upon a previously published paper in which the taxonomy and fundamental strategies for ontology development are established. The article presents the developed ontology and its use for structuring research data in three key use cases, that is, different experiments performed to validate the ontology's capabilities, provide feedback, and ensure its applicability.

Smart Rubber Extrusion Line Combining Multiple Sensor Techniques for AI-Based Process Control

Alexander Aschemann, Paul-Felix Hagen, Simon Albers, Robin Rofallski, Sven Schwabe, Mohammed Dagher, Marco Lukas, Sebastian Leineweber, Benjamin Klie, Patrick Schneider, Hagen Bossemeyer, Lennart Hinz, Markus Kästner, Birger Reitz, Eduard Reithmeier, Thomas Luhmann, Hainer Wackerbarth, Ludger Overmeyer, Ulrich Giese

Abstract

The extrusion process is one of the most important methods for continuous processing of rubber compounds. An extruder is used to give the rubber compound a geometrically defined shape as an extrudate. To ensure that product-specific requirements are fulfilled, the extrusion process and the resulting extrudate are currently monitored using various sensor technologies. Nevertheless, a certain amount of scrap material is produced during the extrusion process, often as a result of unstable process conditions. In this context, one solution for enhancing resource efficiency is the digitalization of the production chain. The aim of this work is to demonstrate an approach for the digitalization of an extrusion line that combines the use of innovative measuring methods for process monitoring and algorithms from the field of artificial intelligence (AI) for process control. For the validation of the individual measuring systems and the process control, various production scenarios in the extrudate production are considered. The results show that the measurement systems for process and extrudate monitoring can directly detect changes in the extrusion process and extrudate quality. Furthermore, the generated data can be used to automatically adjust the extrusion process by the developed AI-based control system.

Performance Evaluation of Upper-Level Ontologies in Developing Materials Science Ontologies and Knowledge Graphs

Hossein Beygi Nasrabadi, Ebrahim Norouzi, Harald Sack, Birgit Skrotzki

Abstract

This study tackles a significant challenge in ontology development for materials science: selecting the most appropriate upper-level ontologies for creating application-level ontologies and knowledge graphs. Focusing on the use case of Brinell hardness testing, the research assesses the performance of various top-level ontologies (TLOs)—basic formal ontology (BFO), elementary multiperspective material ontology (EMMO), and provenance ontology (PROVO)—in developing Brinell testing ontologies (BTOs). Consequently, three versions of BTOs are created using combinations of these TLOs along with their integrated mid- and domain-level ontologies. The performance of these ontologies is evaluated based on ten parameters: semantic richness, domain coverage, extensibility, complexity, mapping efficiency, query efficiency, integration with other ontologies, adaptability to different data contexts, community acceptance, and documentation and maintainability. The results show that all candidate TLOs can effectively develop BTOs, each with its distinct advantages. BFO provides a well-structured, understandable hierarchy, and excellent query efficiency, making it suitable for integration across various ontologies and applications. PROVO demonstrates balanced performance with strong integration capabilities. Meanwhile, EMMO offers high semantic richness and domain coverage, though its complex structure impacts query efficiency and integration with other ontologies.

Automated Workflow for Phase-Field Simulations: Unveiling the Impact of Heat-Treatment Parameters on Bainitic Microstructure in Steel

Dhanunjaya K. Nerella, Muhammad Adil Ali, Hesham Salama, Oguz Gulbay, Marc Ackermann, Oleg Shchyglo, Ulrich Krupp, Ingo Steinbach

Abstract

Bainitic steels are extensively utilized across various sectors, such as the automotive and railway industries, owing to their impressive mechanical properties, including strength, hardness, and fatigue resistance. However, the pursuit of achieving the desired optimal mechanical properties presents considerable challenges due to the intricate bainitic microstructures consisting of multiple phases. To tackle these challenges, an automated workflow is used for extracting 2D and 3D microstructural features. The proposed method allows for a detailed examination of the correlations between microstructure characteristics and the processing parameters, specifically the holding temperature during transformation. In these findings, it is revealed that as the holding temperature decreases, there is a notable reduction in microstructural element size and carbon partitioning. Some of the observations are microstructural features such as area, perimeter, and thickness of the bainitic ferrite grains under two different holding temperatures. Phase-field simulations results show that the microstructures at lower holding temperatures have finer grains. The distributions of grain areas and perimeters are uniform, with smaller grains dominating at low and high isothermal holding temperatures. While the grain thickness measurements from simulations and experiments at high temperature are qualitatively aligned, data from low temperatures show discrepancies.

Digital Methods for the Fatigue Assessment of Engineering Steels

Sascha Fliegener, Johannes Rosenberger, Michael Luke, José Manuel Domínguez, Joana Francisco Morgado, Hans-Ulrich Kobialka, Torsten Kraft, Johannes Tlatlik

Abstract

Engineering steels are used for a wide range of applications in which their fatigue behavior is a crucial design factor. The fatigue properties depend on various influencing factors such as chemical composition, heat treatment, surface properties, load parameters, microstructure, and others. During product development, various material characterization and qualification experiments are mandatory. For a faster and more cost-efficient development, data driven methods (machine learning) promise to replace or to complement material testing by prediction of the fatigue strength. With an ontology-based, semantically-linked knowledge graph, representing the manufacturing history of the material, the influence of the parameters of the process chain on the resulting properties can be accounted for. Herein, it is shown how a fatigue database containing a wide range of materials is assembled from literature. After postprocessing and curation of the data, machine learning predictions of mechanical properties are discussed under multiple aspects. A domain ontology is defined, containing the relevant class definitions for the use case. After applying a data integration and mapping workflow, it is shown how the data can be systematically queried using knowledge graphs describing the manufacturing history of the materials.

Mechanical testing dataset of cast copper alloys for the purpose of digitalization

Hossein Beygi Nasrabadi, Felix Bauer, Patrick Uhlemann, Steffen Thärig, Birgit Rehmer, Birgit Skrotzki

Abstract

This data article presents a set of primary, analyzed, and digitalized mechanical testing datasets for nine copper alloys. The mechanical testing methods including the Brinell and Vickers hardness, tensile, stress relaxation, and low-cycle fatigue (LCF) testing were performed according to the DIN/ISO standards. The obtained primary testing data (84 files) mainly contain the raw measured data along with the testing metadata of the processes, materials, and testing machines. Five secondary datasets were also provided for each testing method by collecting the main meta- and measurement data from the primary data and the outputs of data analyses. These datasets give materials scientists beneficial data for comparative material selection analyses by clarifying the wide range of mechanical properties of copper alloys, including Brinell and Vickers hardness, yield and tensile strengths, elongation, reduction of area, relaxed and residual stresses, and LCF fatigue life. Furthermore, both the primary and secondary datasets were digitalized by the approach introduced in the research article entitled “Toward a digital materials mechanical testing lab” [1]. The resulting open-linked data are the machine-processable semantic descriptions of data and their generation processes and can be easily queried by semantic searches to enable advanced data-driven materials research.

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

Abstract

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

Abstract

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

Abstract

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

Abstract

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

Abstract

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

Abstract

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

Abstract

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

Abstract

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

Abstract

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

Abstract

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 diagrams.net to enable graphical ontology editing. It includes a set of pipeline tools to foster ontology development in diagrams.net, 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)

Abstract

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

Abstract

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

Abstract

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

Abstract

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.