Ingenieurwissenschaften und zugeordnete Tätigkeiten
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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.
By automatically recording as much information as possible in automated laboratory setups, reproducibility and traceability of experiments are vastly improved. This presentation shows what such an approach means for the quality of experiments in an X-ray scattering laboratory and an automated synthesis set-up.
PMD Core Ontology (PMDco)
(2023)
The PMD Core Ontology (PMDco) is a comprehensive framework for representing knowledge that encompasses fundamental concepts from the domains of materials science and engineering (MSE). The PMDco has been designed as a mid-level ontology to establish a connection between specific MSE application ontologies and the domain neutral concepts found in established top-level ontologies. The primary goal of the PMDco is to promote interoperability between diverse domains. PMDco's class structure is both understandable and extensible, making it an efficient tool for organizing MSE knowledge. It serves as a semantic intermediate layer that unifies MSE knowledge representations, enabling data and metadata to be systematically integrated on key terms within the MSE domain. With PMDco, it is possible to seamlessly trace data generation. The design of PMDco is based on the W3C Provenance Ontology (PROV-O), which provides a standard framework for capturing the generation, derivation, and attribution of resources. By building on this foundation, PMDco facilitates the integration of data from various sources and the creation of complex workflows. In summary, PMDco is a valuable tool for researchers and practitioners in the MSE domains. It provides a common language for representing and sharing knowledge, allowing for efficient collaboration and promoting interoperability between diverse domains. Its design allows for the systematic integration of data and metadata, enabling seamless traceability of data generation. Overall, PMDco is a crucial step towards a unified and comprehensive understanding of the MSE domain. PMDco at GitHub: https://github.com/materialdigital/core-ontology
Knowledge representation in the materials science and engineering (MSE) domain is a vast and multi-faceted challenge: Overlap, ambiguity, and inconsistency in terminology are common. Invariant and variant knowledge are difficult to align cross-domain. Generic top-level semantic terminology often is too abstract, while MSE domain terminology often is too specific.
In this poster presentation, an approach how to maintain a comprehensive and intuitive MSE-centric terminology composing a mid-level ontology–the PMD core ontology (PMDco)–via MSE community-based curation procedures is shown.
The PMDco is designed in direct support of the FAIR principles to address immediate needs of the global experts community and their requirements. The illustrated findings show how the PMDco bridges semantic gaps between high-level, MSE-specific, and other science domain semantics, how the PMDco lowers development and integration thresholds, and how to fuel it from real-world data sources ranging from manually conducted experiments and simulations as well as continuously automated industrial applications.
Tensile Test Ontology (TTO)
(2023)
This is the stable version 2.0.1 of the PMD ontology module of the tensile test (Tensile Test Ontology - TTO) as developed on the basis of the 2019 standard ISO 6892-1: Metallic materials - Tensile Testing - Part 1: Method of test at room temperature.
The TTO was developed in the frame of the PMD project. The TTO provides conceptualizations valid for the description of tensile test and corresponding data in accordance with the respective standard. By using TTO for storing tensile test data, all data will be well structured and based on a common vocabulary agreed on by an expert group (generation of FAIR data) which will lead to enhanced data interoperability. This comprises several data categories such as primary data, secondary data and metadata. Data will be human and machine readable. The usage of TTO facilitates data retrieval and downstream usage. Due to a close connection to the mid-level PMD core ontology (PMDco), the interoperability of tensile test data is enhanced and data querying in combination with other aspects and data within the broad field of material science and engineering (MSE) is facilitated.
The TTO class structure forms a comprehensible and semantic layer for unified storage of data generated in a tensile test including the possibility to record data from analysis, re-evaluation and re-use. Furthermore, extensive metadata allows to assess data quality and reproduce experiments. Following the open world assumption, object properties are deliberately low restrictive and sparse.
The efforts taken within the project ‘platform MaterialDigital’ (PMD, materialdigital.de) to store FAIR data in accordance with a standard-compliant ontological representation (‘application ontology’) of a tensile test of metals at room temperature (ISO 6892-1:2019-11) will be presented. This includes the path from developing an ontology in accordance with the respective standard, converting ordinary data obtained from standard tests into the interoperable RDF format, up to connecting the ontology and data. The semantic connection of the ontology and data leads to interoperability and an enhanced ability of querying. For further reusability of data and knowledge semantically stored, the PMD core ontology (PMDco) was developed, which is a mid-level ontology in the field of MSE. The semantic connection of the tensile test application ontology to the PMDco is also presented. Moreover, Ontopanel, a tool for domain experts facilitating visual ontology development and mapping for FAIR data sharing in MSE, is introduced briefly.
Das hochaktuelle Thema der Integration und Wiederverwendung von Wissen und Daten aus Herstellung, Bearbeitung und Charakterisierung von Materialien ('Digitalisierung von Materialien') wird in den Projekten Innovationsplattform MaterialDigital (PMD, materialdigital.de) und Materials-open-Lab (Mat-o-Lab, matolab.org) adressiert. In diesem Beitrag werden die Weiterentwicklungen in diesen Projekten hinsichtlich der Speicherung von Zugversuchsdaten gemäß einer normenkonformen (DIN EN ISO 6892-1:2019-11) ontologischen Repräsentation vorgestellt. Das umfasst den Weg von der Entwicklung einer Ontologie nach Norm, der Konvertierung von Daten aus Standardtests in das interoperable RDF-Format bis hin zur Verknüpfung von Ontologie und Daten. Letztendlich können die entsprechenden Daten in einem Triple Store abgelegt und abgefragt werden.
Grain boundaries (GBs) are planar lattice defects that govern the properties of many types of polycrystalline materials. Hence, their structures have been investigated in great detail. However, much less is known about their chemical features, owing to the experimental difficulties to probe these features at the atomic length scale inside bulk material specimens. Atom probe tomography (APT) is a tool capable of accomplishing this task, with an ability to quantify chemical characteristics at near-atomic scale. Using APT data sets, we present here a machine-learning-based approach for the automated quantification of chemical features of GBs. We trained a convolutional neural network (CNN) using twenty thousand synthesized images of grain interiors, GBs, or triple junctions. Such a trained CNN automatically detects the locations of GBs from APT data. Those GBs are then subjected to compositional mapping and analysis, including revealing their in-plane chemical decoration patterns. We applied this approach to experimentally obtained APT data sets pertaining to three case studies, namely, Ni-P, Pt-Au, and Al-Zn-Mg-Cu alloys. In the first case, we extracted GB specific segregation features as a function of misorientation and coincidence site lattice character. Secondly, we revealed interfacial excesses and in-plane chemical features that could not have been found by standard compositional analyses. Lastly, we tracked the temporal evolution of chemical decoration from early-stage solute GB segregation in the dilute limit to interfacial phase separation, characterized by the evolution of complex composition patterns. This machine-learning-based approach provides quantitative, unbiased, and automated access to GB chemical analyses, serving as an enabling tool for new discoveries related to interface thermodynamics, kinetics, and the associated chemistry-structure-property relations.
The presentation shows the current development status of the Welding Data Exchange (weldx) format based on the Advanced Scientific Data Format (ASDF).
The use of a complete single pass arc welding experiment example stored in a single weldx file and validated against a predefined schema definition is presented.
The example includes generic experimental metadata, the workpiece geometry and materials definition following associated standards, the weld process spatial movement description, the welding process parameter descriptions and welding process measurements. The inclusion of 3D scan data of the workpiece description is also included.
The full code and data is available on GitHub: https://github.com/BAMWelDX/IIW2021_AA_CXII
The increasing adoption of Open Science principles has been a prevalent topic in the welding science community over the last years. Providing access to welding knowledge in the form of complex and complete datasets in addition to peer-reviewed publications can be identified as an important step to promote knowledge exchange and cooperation. There exist previous efforts on building data models specifically for fusion welding applications; however, a common agreed upon implementation that is used by the community is still lacking. One proven approach in other domains has been the use of an openly accessible and agreed upon file and data format used for archiving and sharing domain knowledge in the form of experimental data. Going into a similar direction, the welding community faces particular practical, technical, and also ideological challenges that are discussed in this paper. Collaboratively building upon previous work with modern tools and platforms, the authors motivate, propose, and outline the use of a common file format specifically tailored to the needs of the welding research community as a complement to other already established Open Science practices. Successfully establishing a culture of openly accessible research data has the potential to significantly stimulate progress in welding research.
The presentation shows the current development status of the Welding Data Exchange (weldx) format based on the Advanced Scientific Data Format (ASDF).
The use of a complete single pass arc welding experiment example stored in a single weldx file and validated against a predefined schema definition is presented.
The example includes generic experimental metadata, the workpiece geometry and materials definition following associated standards, the weld process spatial movement description, the welding process parameter descriptions and welding process measurements. The inclusion of 3D scan data of the workpiece description is also included.
The full code and data is available on GitHub: https://github.com/BAMWelDX/IIW2021_joint_intermediate_CXII
Scientific welding data covers a wide range of physical domains and timescales and are measured using various different sensors. Complex and highly specialized experimental setups at different welding institutes complicate the exchange of welding research data further. The project aims to simplify the exchange and quality assurance of scientific welding data by establishing a common open source file format.
The WelDX research project aims to foster the exchange of scientific data inside the welding community by developing and establishing a new open source file format suitable for documentation of experimental welding data and upholding associated quality standards. In addition to fostering scientific collaboration inside the national and international welding community an associated advisory committee will be established to oversee the future development of the file format. The proposed file format will be developed with regards to current needs of the community regarding interoperability, data quality and performance and will be published under an appropriate open source license.
By using the file format objectivity, comparability and reproducibility across different institutes and experimental setups can be improved.
WelDX is a newly started research project at BAM publicly funded by the German Federal Ministry of Education and Research. Over the course of three years the project aims to build the foundation for a publicly accessible file format and to foster research collaboration in arc welding on a national and international level.
The talk will motivate benefits and discuss challenges of using a common file format designed to describe, store and share arc welding research data in the scope of "Open Science". By using common open source software and tools, welding data will be made more accessible and reusable so that new scientific practices may emerge.
The proposed file format aims to be easy to use for common welding applications while also offering the possibility to describe complex experiments for state of the art welding research.
In addition the talk will illustrate how in the future other facilities and researchers will be able to use experimental arc welding data generated at BAM for their own research, for example to conduct their own data analysis or welding process and thermo-mechanical simulations.
The talk will give an overview of past, ongoing and future activities at BAM welding divisions, specifically those regarding prospects and challenges of the ongoing digital transformation and the move to more accessible research data (open science) with concerns to welding research. We will discuss current ways to publish and share research results inside the welding community and highlight approaches and advancements from other scientific fields to improve accessibility and reproducibility. We would also like to discuss the feasibility of integrating open science principles into the current IIW landscape of meetings, publications and education. In Addition we will introduce our upcoming series of workshops organized by BAM focusing on defining and implementing an open source file format specifically designed to publish and exchange high quality welding research data.
The digitalization of industrial processes is the most discussed topic in society these days. New business models have been developed to benefit from the opportunities offered by a digitally connected world. However, the focus in on the smart factory consisting of autonomous acting cyber physical systems (CPS). The efficient implementation of such CPS within an industrial environment requires the digitalization of the corresponding production processes. The digital twin of the process under investigation enables to develop sophisticated monitoring and control strategies which are necessary to fulfil the requirements of individual product design.
The need for a digitalization of the welding process is a logical consequence especially with regards to its industrial importance. The theoretical investigations and derived mathematical models of the welding process are well known since many decades. Anyhow, there is still a lack of industrial applicability of such models for an efficient and safe design of welded components. With respect to structural welding simulation that targets the heat effects of welding in terms of global quantities like temperature, solid phase distribution and residual stresses as well as distortions, the limited predictability of these quantities for arbitrary process parameters hinders its usability.
This presentation aims to give an overview of the current state of the art in structural welding simulation to predict the evolution of welding induced temperatures, stresses and distortions. Emphasis is given to industrial applicability of such models by reduction of the calculation times for large real-world structures and improved prediction of optimal process parameters. Furthermore, the role of such models within a weld data management system is demonstrated. The accumulation and relational storage of simulation and measuring data improves the overall process knowledge. This enables virtual cause and effect analyses of new process parameters as basis for a control system design.