KadiStudio: FAIR Modelling of Scientific Research Processes

FAIR handling of scientific data plays a significant role in current efforts towards a more sustainable research culture and serves as a prerequisite for the fourth scientific paradigm, that is, data-driven research. To enforce the FAIR principles by ensuring the reproducibility of scientific data and tracking their provenance comprehensibly, the FAIR modelling of research processes in form of automatable workflows is necessary. By providing reusable procedures containing expert knowledge, such workflows contribute decisively to the quality and the acceleration of scientific research. In this work, the requirements for a system to be capable of modelling FAIR workflows are defined and a generic concept for modelling research processes as workflows is developed. For this, research processes are iteratively divided into impartible subprocesses at different detail levels using the input-process-output model. The concrete software implementation of the identified, universally applicable concept is finally presented in form of the workflow editor KadiStudio of the Karlsruhe Data Infrastructure for Materials Science (Kadi4Mat)

Kadi4Mat : A Research Data Infrastructure for Materials Science

The concepts and current developments of a research data infrastructure for materials science are presented, extending and combining the features of an electronic lab notebook and a repository. The objective of this infrastructure is to incorporate the possibility of structured data storage and data exchange with documented and reproducible data analysis and visualization, which finally leads to the publication of the data. This way, researchers can be supported throughout the entire research process. The software is being developed as a web-based and desktop-based system, offering both a graphical user interface and a programmatic interface. The focus of the development is on the integration of technologies and systems based on both established as well as new concepts. Due to the heterogeneous nature of materials science data, the current features are kept mostly generic, and the structuring of the data is largely left to the users. As a result, an extension of the research data infrastructure to other disciplines is possible in the future. The source code of the project is publicly available under a permissive Apache 2.0 license

Characterization of porous membranes using artificial neural networks

Porous membranes have been utilized intensively in a wide range of fields due to their special characteristics and a rigorous characterization of their microstructures is crucial for understanding their properties and improving the performance for target applications. A promising method for the quantitative analysis of porous structures leverages the physics-based generation of porous structures at the pore scale, which can be validated against real experimental microstructures, followed by building the process–structure–property relationships with data-driven algorithms such as artificial neural networks. In this study, a Variational AutoEncoder (VAE) neural network model is used to characterize the 3D structural information of porous materials and to represent them with low-dimensional latent variables, which further model the structure–property relationship and solve the inverse problem of process–structure linkage combined with the Bayesian optimization method. Our methods provide a quantitative way to learn structural descriptors in an unsupervised manner which can characterize porous microstructures robustly

A U-Net-Based Self-Stitching Method for Generating Periodic Grain Structures

When modeling microstructures, the computational resource requirements increase rapidly as the simulation domain becomes larger. As a result, simulating a small representative fraction under periodic boundary conditions is often a necessary simplification. However, the truncated structures leave nonphysical boundaries, which are detrimental to numerical modeling. Here, we propose a self-stitching algorithm for generating periodic structures, demonstrated in a grain structure. The main idea of our algorithm is to artificially add structural information between mismatched boundary pairs, using the hierarchical spatial predictions of the U-Net. The algorithm provides an automatic and unbiased way to obtain periodic boundaries in grain structures and can be applied to porous microstructures in a similar way

KadiStudio Use-Case Workflow: Automation of Data Processing for in Situ Micropillar Compression Tests

Scientific processes produce huge amounts of data that are usually acquired, transformed and analyzed on a regular basis. Translating these processes into automatable and reproducible workflows is considered to be an efficient way to support scientists in performing repeated processes that would otherwise be time-consuming and error-prone tasks. Consequently, the quality of scientific research can be accelerated and enhanced. In this article, we present for the first time a use-case of KadiStudio as a tool to automate analysis procedures of scientific data that are repeatedly acquired from in situ scanning electron microscope (SEM) micromechanical testing. KadiStudio provides a desktop-based workflow editor as part of the ecosystem of Kadi4Mat: Karlsruhe Data Infrastructure for material Science. The presented workflow includes nodes for processing and analysis of different types of data, namely mechanical response in text format and a series of SEM images in video file format acquired during in situ SEM deformation tests. In addition, the raw and analyzed data are automatically uploaded to the KadiWeb repository via nodes based on the kadi-apy library

Herausforderungen und Chancen von Wissenschaftskommunikation in den Gesellschaftswissenschaften

Mit der zunehmenden Ökonomisierung sowie der Vermessung und Digitalisierung von Wissenschaft steht der akademische Sektor vor enormen Herausforderungen. Der Bedarf der Öffentlichkeit an wissenschaftlicher Expertise und die Erwartungen an eine faktenbasierte Politik sprechen alle Disziplinen an, dies ist nicht erst seit der Coronavirus-Pandemie offensichtlich. Vor diesem Hintergrund sind »Wissenschaftskommunikation« und »Public Science« zu eigenständigen und zunehmend geforderten Elementen akademischer Praxis geworden. Die disziplinären Besonderheiten erfordern jedoch differenzierte Analysen dieser neuen Entwicklung. Der Workshop zu Herausforderungen und Chancen von Wissenschaftskommunikation in den Gesellschaftswissenschaften hat sich in diesem Lichte auf die Erkenntnisse aus der (soziologischen) Wissenschaftsforschung konzentriert. Der vorliegende Text dokumentiert die Abschlussdiskussion. With the increasing economization as well as the ›governance by numbers‹ and digitalization of science, the academic sector faces tremendous challenges. The public’s need for scientific knowledge and expectations of fact-based policies affect all disciplines; this has been obvious not only since the coronavirus pandemic. In this light, »science communication« and notions of »public science« have become independent and increasingly demanded elements of research and teaching. However, the disciplinary characteristics require differentiated analyses of this new development. The workshop on challenges and opportunities of science communication in the social sciences focused on the findings of (sociological) science research. This article documents the closing discussion

Predicting mechanical properties of Foam structures using Machine Learning

The use of composite materials such as a polyurethane aluminium sandwich structure is a promising method for weight reduction in novel vehicle concepts. Dimensioning such composite components, however, requires knowledge of the mechanical properties of the foam used. These properties are usually determined with the help of different mechanical testing methods. In order to replace these time- and cost-intensive experiments, the project presented here aims to develop a machine learning (ML) approach that identifies structure-property linkages in foams and can thus predict their mechanical properties based on the microstructure. To generate a suitable database for the training of an ML-algorithm, both experimental investigations of different foam structures are carried out and computational methods are applied. Via the experiments the mechanical properties of the foam structures are determined by means of tensile and compression tests while computer tomographic (CT) measurements are used to obtain high resolution images of the used foam samples. The resulting CT-scans are converted into digital representations of the microstructures and mechanical simulations as well as image analysis algorithms are applied using the PACE3D [1] simulation framework. In this way the used simulation model is validated. Additionally, new insights into the morphology of the foam structure are gained by extracting structure parameters such as the mean pore size, wall thickness and porosity. Further 3D foam structures are generated algorithm-based with defined structure parameters and their mechanical properties are computationally determined. The generated structures and their corresponding mechanical properties serve as the basis for training a suitable machine learning algorithm. Its implementation is realised within the framework of the research data infrastructure Kadi4Mat [2], which enables the structured storage of the created data as well as the implementation of the machine learning algorithm through automatable workflows. Kadi4Mat further enables the exchange of results and their comprehensible documentation through the use of metadata and ontologies. To enable both the reproducibility of the results and the transfer of the developed methodologies to other applications, all processes developed in the project are additionally archived in Kadi4Mat in the form of automatable workflows

Identifying structure-property linkages in polyurethane foams to characterize their mechanical properties using machine learning

The design of sandwich composites with a polyurethane foam core and a metallic face material, requires the knowledge of the mechanical properties of the constituent materials. These are generally known for metallic materials, but have to be determined for plastic foams, usually via experiments as they are greatly dependent on the foam‘s microstructure. In order to substitute these time-consuming and cost-intensive experiments, this work presents a procedure for characterising the mechanical properties of plastic foams by identifying structure-property linkages using machine learning. The basis for this are experimentally validated simulations of reconstructed and algorithm-based generated digital-twins of polyurethane foam structures. The microstructures of these generated foam structures are varied systematically to create an information-rich data-basis thereby obtaining an accurate and robust machine-learning tool

Correlation between microstructural and macroscopic mechanical properties of polyurethane foams

Mechanical properties of structural foams are largely influenced by the manufacturing process. Depending on the manufacturing process, the foam type (e.g. open-pored/closed-pored), density and the foam orientation are defined. However, these quantities only define the global structural properties. Due to the inhomogeneous process, no adequate statement can yet be made about the real microstructure. However, if a mechanical load is applied, the generated inhomogeneous stress field depends on the actual microstructure, which is characterized by specific values such as pore distribution, mean pore size, spatial gradients in the pore size distribution, pore arrangement and geometry of cell walls. This can lead to local differences in mechanical properties due to irregularities generated by the foaming process. The objective of the current research is linking the above-mentioned microstructural properties and the resulting macroscopic mechanical material behavior of structural foams by using artificial intelligence (AI) based digital methods. Therefore, polyurethane (PU)-foams of varying densities are analyzed to create a suitable database. For each of the investigated foam densities, the dependency of the microstructure and the mechanical properties according to the sampling position are investigated. First, the mechanical properties of tension and compression load are experimentally determined in parallel and perpendicular spatial direction as well as for varying positions along the foaming directions. The microstructure properties of the real PU-foams are determined using computer tomography scans. Afterwards, the microstructures are reconstructed using computer algorithms developed with the microstructure simulation package Pace3D in order to generate digital twins. Applying various data science methods and micromechanics simulations, the morphological characteristics and the mechanical properties of the digital microstructures are determined. The results from experiments and computational methods are compared and correlations between the mechanical tests and the microstructure analyses are derived. In forthcoming research, this correlation of microstructure and mechanical behavior will be explained and predicted using AI-based methods