Special Issue

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.

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.

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).

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.

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.

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.

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.

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.

A key aspect in the development of multilayer inductors is the magnetic permeability of the ferrite layers. Herein, the effects of different processing steps on the permeability of a NiCuZn ferrite are investigated. Dry-pressed, tape-cast, and cofired multilayer samples are analyzed. An automated data pipeline is applied to structure the acquired experimental data according to a domain ontology based on Platform MaterialDigital core ontology. Example queries to the ontology show how the determined process–property correlations are accessible to nonexperts and thus how suitable data for component design can be identified. It is demonstrated how the inductance of cofired multilayer inductors is reliably predicted by simulations if the appropriate input data corresponding to the manufacturing process is used.

Automated computational workflows are a powerful concept that can improve the usability and reproducibility of simulation and data processing approaches. Although used very successfully in bioinformatics, workflow environments in materials science are currently commonly applied in the field of atomistic simulations. This work showcases the integration of a discrete element method (DEM) simulation of powder pressing in the convenient SimStack workflow environment. For this purpose, a Workflow active Node (WaNo) was developed to generate input scripts for the DEM solver using LIGGGHTS Open Source Discrete Element Method Particle Simulation code. Combining different WaNos in the SimStack framework makes it possible to build workflows and loop over different simulation or evaluation conditions. The functionality of the workflows is explained, and the added user value is discussed. The procedure presented here is an example and template for many other simulation methods and issues in materials science and engineering.

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.

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.

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.