Special Issue

Here you will find all publications within the special issue "Digitalization in Materials Science and Engineering" in Advanced Engineering Materials (AEM)

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.

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.