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The volume of program source code created, reused, and maintained worldwide is rapidly increasing, yet code comprehension remains a limiting productivity factor. For developers and maintainers, well known common software design patterns and the abstractions they offer can help support program comprehension. However, manual pattern documentation techniques in code and code-related assets such as comments, documents, or models are not necessarily consistent or dependable and are cost-prohibitive. To address this situation, we propose the Hybrid Design Pattern Detection (HyDPD), a generalized approach for detecting patterns that is programming-language-agnostic and combines graph analysis (GA) and Machine Learning (ML) to automate the detection of design patterns via source code analysis. Our realization demonstrates its feasibility. An evaluation compared each technique and their combination for three common patterns across a set of 75 single pattern Java and C# public sample pattern projects. The GA component was also used to detect the 23 Gang of Four design patterns across 258 sample C# and Java projects as well as in a large Java project. Performance and scalability were measured. The results show the advantages and potential of a hybrid approach for combining GA with artificial neural networks (ANN) for automated design pattern detection, providing compensating advantages such as reduced false negatives and improved F1 scores.
Today’s Industry 4.0 Smart Factories involve complicated and highly automated processes. Nevertheless, certain crucial activities such as machine maintenance remain that require human involvement. For such activities, many factors have to be taken into account, like worker safety or worker qualification. This adds to the complexity of selection and assignment of optimal human resources to the processes and overall coordination. Contemporary Business Process Management (BPM) Systems only provide limited facilities regarding activity resource assignment. To overcome these, this contribution pro- poses a BPM-integrated approach that applies fuzzy sets and rule processing for activity assignment. Our findings suggest that our approach has the potential for improved work distribution and cost savings for Industry 4.0 production processes. Furthermore, the scalability of the approach provides efficient performance even with a large number of concurrent activity assignment requests and can be applied to complex production scenarios with minimal effort.
Although production processes in Industry 4.0 set- tings are highly automated, many complicated tasks, such as machine maintenance, continue to be executed by human workers. While smart factories can provide these workers with some digitalization support via Augmented Reality (AR) devices, these AR tasks depend on many contextual factors, such as live data feeds from machines in view, or current work safety conditions. Although currently feasible, these localized contextual factors are mostly not well-integrated into the global production process, which can result in various problems such as suboptimal task assignment, over-exposure of workers to hazards such as noise or heat, or delays in the production process. Current Business Process Management (BPM) Systems (BPMS) were not particularly designed to consider and integrate context-aware factors during planning and execution. This paper describes the AR-Process Framework (ARPF) for extending a BPMS to support context-integrated modeling and execution of processes with AR tasks in industrial use cases. Our realization shows how the ARPF can be easily integrated with prevalent BPMS. Our evaluation findings from a simulation scenario indicate that ARPF can improve Industry 4.0 processes with regard to AR task execution quality and cost savings.
With the increasing pressure to deliver additional software functionality, software engineers and developers are often confronted with the dilemma of sufficient software testing. One aspect to avoid is test redundancy, and measuring test (or code or statement) coverage can help focus test development on those areas that are not yet sufficiently tested. As software projects grow, it can be difficult to visualize both the software product and the software testing area and their dependencies. This paper contributes VR-TestCoverage, a Virtual Reality (VR) solution concept for visualizing and interacting with test coverage, test results, and test dependency data in VR. Our VR implementation shows its feasibility. The evaluation results based on a case study show its support for three testing-related scenarios.
The increasing demand for software functionality necessitates an increasing amount of program source code that is retained and managed in version control systems, such as Git. As the number, size, and complexity of Git repositories increases, so does the number of collaborating developers, maintainers, and other stakeholders over a repository’s lifetime. In particular, visual limitations of Git tooling hampers repository comprehension, analysis, and collaboration across one or multiple repositories with a larger stakeholder spectrum. This paper contributes VR-Git, a Virtual Reality (VR) solution concept for visualizing and interacting with Git repositories in VR. Our prototype realization shows its feasibility, and our evaluation results based on a case study show its support for repository comprehension, analysis, and collaboration via branch, commit, and multi-repository scenarios.
Repeatable processes are fundamental for describing how enterprises and organizations operate, for production, for Industry 4.0, etc. As digitalization and automation progresses across all organizations and industries, including enterprises, business, government, manufacturing, and IT, evidence-based comprehension and analysis of the processes involved, including their variations, anomalies, and performance, becomes vital for an increasing set of stakeholders. Process Mining (PM) relies on logs or processes (as such evidence-based) to provide process-centric analysis data, yet insights are not necessarily visually accessible for a larger set of stakeholders (who may not be process or data analysts). Towards addressing certain challenges described in the Process Mining Manifesto, this paper contributes VR-ProcessMine, a solution for visualizing and interacting with PM results in Virtual Reality (VR). Our realization shows its feasibility, and a case-based evaluation provides insights into its capabilities.
As systems grow in complexity, the interdisciplinary nature of systems engineering makes the visualization and comprehension of the underlying system models challenging for the various stakeholders. This, in turn, can affect validation and realization correctness. Furthermore, stakeholder collaboration is often hindered due to the lack of a common medium to access and convey these models, which are often partitioned across multiple 2D diagrams. This paper contributes VR-SysML, a solution concept for visualizing and interacting with SysML models in Virtual Reality (VR). Our prototype realization shows its feasibility, and our evaluation results based on a case study shows its support for the various SysML diagram types in VR, cross-diagram element recognition via our backplane followers concept, and depicting further related (SysML and non-SysML) models side-by-side in VR.
In the fast-growing but also highly competitive market of battery-powered power tools, cell-pack-cooling systems are of high importance, as they guarantee safety and short charging times. A simulation model of an 18 V power tool battery pack was developed to be able to evaluate four different pack-cooling systems (two heat-conductive polymers, one phase change material, and non-convective air as reference) in an application scenario of practical relevance (the intensive use of a power tool followed by cooling down and charging steps). The simulation comprises battery models of 21700 cells that are commercially available as well as heat transfer models. The study highlights the performance of the different cooling materials and their effect on the maximum pack temperature and total charging cycle time. Key material parameters and their influence on the battery pack temperature and temperature homogeneity are discussed. Using phase change materials and heat-conductive polymers, a significantly lower maximum temperature during discharge (up to 26%) and a high shortening potential of the use/charging cycle (up to 32%) were shown. In addition to the cooling material sweep, a parameter sweep was performed, varying the external temperature and air movement. The high importance of the conditions of use on the cooling system’s performance was illustrated.
Transformations in the work–nonwork interface highlight the importance of effectively managing the boundaries between life domains. However, do the ways individuals manage the boundaries between work and nonwork life change from one day to the next? If so, which antecedents may explain these intra-individual fluctuations in boundary management? Drawing on boundary management, spillover, and resource theories, we investigate daily changes in segmentation preferences and integration enactments as a function of experiencing strain in work and nonwork life. Assuming that changes in segmentation preferences reflect an individual’s strategy to regulate negative cross-role spillover, we suppose that strain increases individuals’ segmentation preferences; at the same time, however, it could force individuals to enact more integration.
Identification and quantitative segmentation of individual blood vessels in mice visualized with preclinical imaging techniques is a tedious, manual or semiautomated task that can require weeks of reviewing hundreds of levels of individual data sets. Preclinical imaging, such as micro-magnetic resonance imaging (μMRI) can produce tomographic datasets of murine vasculature across length scales and organs, which is of outmost importance to study tumor progression, angiogenesis, or vascular risk factors for diseases such as Alzheimer’s. Training a neural network capable of accurate segmentation results requires a sufficiently large amount of labelled data, which takes a long time to compile. Recently, several reasonably automated approaches have emerged in the preclinical context but still require significant manual input and are less accurate than the deep learning approach presented in this paper—quantified by the Dice score. In this work, the implementation of a shallow, three-dimensional U-Net architecture for the segmentation of vessels in murine brains is presented, which is (1) open-source, (2) can be achieved with a small dataset (in this work only 8 μMRI imaging stacks of mouse brains were available), and (3) requires only a small subset of labelled training data. The presented model is evaluated together with two post-processing methodologies using a cross-validation, which results in an average Dice score of 61.34% in its best setup. The results show, that the methodology is able to detect blood vessels faster and more reliably compared to state-of-the-art vesselness filters with an average Dice score of 43.88% for the used dataset.
VR-V&V
(2023)
To build quality into a software (SW) system necessitates supporting quality-related lifecycle activities during the software development. In software engineering, software Verification and Validation (V&V) processes constitute an inherent part of Software Quality Assurance (SQA) processes. A subset of the V&V activities involved are: 1) bidirectional traceability analysis of requirements to design model elements, and 2) software testing. Yet the complex nature of large SW systems and the dependencies involved in both design models and testing present a challenge to current V&V tools and methods regarding support for trace analysis. One of software’s essential challenges remains its invisibility, which also affects V&V activities. This paper contributes VR-V&V, a Virtual Reality (VR) solution concept towards supporting immersive V&V activities. By visualizing requirements, models, and testing artifacts with dependencies and trace relations immersively, they are intuitively accessible to a larger stakeholder audience such as SQA personnel while supporting digital cognition. Our prototype realization shows the feasibility of supporting immersive bidirectional traceability as well as immersive software test coverage and analysis. The evaluation results are based on a case study demonstrating its capabilities, in particular traceability support was performed with ReqIF, ArchiMate models, test results, test coverage, and test source to test target dependencies.
Software design patterns and the abstractions they offer can support developers and maintainers with program code comprehension. Yet manually-created pattern documentation within code or code-related assets, such as documents or models, can be unreliable, incomplete, and labor-intensive. While various Design Pattern Detection (DPD) techniques have been proposed, industrial adoption of automated DPD remains limited. This paper contributes a hybrid DPD solution approach that leverages a Bayesian network integrating developer expertise via rule-based micropatterns with our machine learning subsystem that utilizes graph embeddings. The prototype shows its feasibility, and the evaluation using three design patterns shows its potential for detecting both design patterns and variations.
VR-SysML+Traceability
(2023)
As systems grow in complexity, the interdisciplinary nature of systems engineering makes the visualization and comprehension of the underlying system models challenging for the various stakeholders. This, in turn, can affect validation and realization correctness. Furthermore, stakeholder collaboration is often hindered due to the lack of a common medium to access and convey these models, which are often partitioned across multiple 2D diagrams. This paper contributes VR-SysML, a solution concept for visualizing and interacting with Systems Modeling Language (SysML) models in Virtual Reality (VR). Our prototype realization shows its feasibility, and our evaluation results based on a case study shows its support for the various SysML diagram types in VR, cross-diagram element recognition via our Backplane Followers concept, and depicting further related (SysML and non-SysML) models side-by-side in VR.
VR-GitCity
(2023)
The increasing demand for software functionality necessitates an increasing amount of program source code that is retained and managed in version control systems, such as Git. As the number, size, and complexity of Git repositories increases, so does the number of collaborating developers, maintainers, and other stakeholders over a repository’s lifetime. In particular, visual limitations of command line or two- dimensional graphical Git tooling can hamper repository comprehension, analysis, and collaboration across one or multiple repositories when a larger stakeholder spectrum is involved. This is especially true for depicting repository evolution over time. This paper contributes VR-GitCity, a Virtual Reality (VR) solution concept for visualizing and interacting with Git repositories in VR. The evolution of the code base is depicted via a 3D treemap utilizing a city metaphor, while the commit history is visualized as vertical planes. Our prototype realization shows its feasibility, and our evaluation results based on a case study show its depiction, comprehension, analysis, and collaboration capabilities for evolution, branch, commit, and multi-repository analysis scenarios.
VR-EDStream+EDA
(2023)
With increasing digitalization, the importance of data and events, which comprise its most fundamental level, cannot be overemphasized. All types of organizations, including enterprises, business, government, manufacturing, and the supporting IT, are dependent on these fundamental building blocks. Thus, evidence-based comprehension and analysis of the underlying data and events, their stream processing, and correlation with enterprise events and activities becomes vital for an increasing set of (grassroot or citizen) stakeholders. Thus, further investigation of accessible alternatives to visually support analysis of data and events is needed. This paper contributes VR-EDStream+EDA, a solution for immersively visualizing and interacting with data and event streams or pipelines and generically visualizing Event-Driven Architecture (EDA) in Virtual Reality (VR). Our realization shows its feasibility, and a case-based evaluation provides insights into its capabilities.
The surface topography of biodegradable polymer foils is modified by mechanical imprinting on a submillimeter length scale. The created patterns strongly influence the wetting behavior and allow the preparation of hydrophobic surfaces with controlled solid-liquid interaction. A detailed analysis of anisotropic surface patterns reveals that the observed effect arises from a combination of topographical and compositional changes that are introduced to the surface. As a main result it is found that an individual combination of material and structure is required for the production of water-repellent biopolymer foils that are highly attractive for packaging applications.
Laser melting manufacturing of large elements of lunar regolith simulant for paving on the Moon
(2023)
The next steps for the expansion of the human presence in the solar system will be taken on the Moon. However, due to the low lunar gravity, the suspended dust generated when lunar rovers move across the lunar soil is a significant risk for lunar missions as it can affect the systems of the exploration vehicles. One solution to mitigate this problem is the construction of roads and landing pads on the Moon. In addition, to increase the sustainability of future lunar missions, in-situ resource utilization (ISRU) techniques must be developed. In this paper, the use of concentrated light for paving on the Moon by melting the lunar regolith is investigated. As a substitute of the concentrated sunlight, a high-power CO2 laser is used in the experiments. With this set-up, a maximum laser spot diameter of 100 mm can be achieved, which translates in high thicknesses of the consolidated layers. Furthermore, the lunar regolith simulant EAC-1A is used as a substitute of the actual lunar soil. At the end of the study, large samples (approximately 250 × 250 mm) with interlocking capabilities were fabricated by melting the lunar simulant with the laser directly on the powder bed. Large areas of lunar soil can be covered with these samples and serve as roads and landing pads, decreasing the propagation of lunar dust. These manufactured samples were analysed regarding their mineralogical composition, internal structure and mechanical properties.
Future lunar exploration will be based on in-situ resource utilization (ISRU) techniques. The most abundant raw material on the Moon is lunar regolith, which, however, is very scarce on Earth, making the study of simulants a necessity. The objective of this study is to characterize and investigate the sintering behavior of EAC-1A lunar regolith simulant. The characterization of the simulant included the determination of the phase assemblage, characteristic temperatures determination and water content analysis. The results are discussed in the context of sintering experiments of EAC-1A simulant, which showed that the material can be sintered to a relative density close to 90%, but only within a very narrow range of temperatures (20–30 °C). Sintering experiments were performed for sieved and unsieved, as well as for dried and non-dried specimens of EAC-1A. In addition, an analysis of the densification and mechanical properties of the sintered specimens was done. The sintering experiments at different temperatures showed that the finest fraction of sieved simulant can reach a higher maximum sintering temperature, and consequently a higher densification and biaxial strength. The non-dried powder exhibited higher densification and biaxial strength after sintering compared to the dried specimen. This difference was explained with a higher green density of the non-dried powder during pressing, rather than due to an actual influence on the sintering mechanism. Nevertheless, drying the powder prior to sintering is important to avoid the overestimation of the strength of specimens to be fabricated on the Moon.
Soft magnetic Fe-Al alloys have been a subject of research in the past. However, they never saw the same reception in technical applications as the Fe-Si or Fe-Ni alloys, which is, to some extent, due to a low ductility level and difficulties in manufacturing. Additive manufacturing (AM) technology could be a way to avoid issues in conventional manufacturing and produce soft magnetic components from these alloys, as has already been shown with similarly brittle Fe-Si alloys. While AM has already been applied to certain Fe-Al alloys, no magnetic properties of AM Fe-Al alloys have been reported in the literature so far. Therefore, in this work, a Fe-12Al alloy was additively manufactured through laser powder bed fusion (L-PBF) and characterized regarding its microstructure and magnetic properties. A comparison was made with the materials produced by casting and rolling, prepared from melts with an identical chemical composition. In order to improve the magnetic properties, a heat treatment at a higher temperature (1300 °C) than typically applied for conventionally manufactured materials (850–1150 °C) is proposed for the AM material. The specially heat-treated AM material reached values (HC: 11.3 A/m; µmax: 13.1 × 103) that were close to the heat-treated cast material (HC: 12.4 A/m; µmax: 20.3 × 103). While the DC magnetic values of hot- and cold-rolled materials (HC: 3.2 to 4.1 A/m; µmax: 36.6 to 40.4 × 103) were not met, the AM material actually showed fewer losses than the rolled material under AC conditions. One explanation for this effect can be domain refinement effects. This study shows that it is possible to additively manufacture Fe-Al alloys with good soft magnetic behavior. With optimized manufacturing and post-processing, further improvements of the magnetic properties of AM L-PBF Fe-12Al may still be possible.
Based on a data-driven approach, a computer-assisted workflow for the quantitative analysis of optical Kerr microscopy images of sintered FeNdB-type permanent magnets was developed. By analyzing the domain patterns visible in the Kerr image with data-driven approaches such as traditional machine learning and advanced deep learning, we can quantify grain orientation and size with a better trade-off between accuracy and higher throughput than electron backscatter diffraction (EBSD). The key distinction between traditional machine learning and advanced deep learning lies in feature extraction. Traditional methods require manual, user-dependent feature extraction from input data, while advanced deep learning achieves this automatically. The predictions from the trained models were compared to the measurements from EBSD for performance evaluation. The proposed data-driven model is trained on the dataset created from the correlative microscopy technique, which requires the images of grains extracted from the Kerr microscopy and corresponding EBSD grain orientation data (Euler angles). The fine-tuned deep learning model shows better generalization ability than the traditional machine learning models trained on the manually extracted features and resulted in a mean absolute error of less than 5° for grain orientation of the anisotropic magnet samples when evaluated against the measured EBSD values. The developed approach has reduced the measurement effort for grain orientation by 5 times and have sufficient accuracy when compared to the EBSD.
In this study, we investigate the use of artificial neural networks as a potentially efficient method to determine the rate capability of electrodes for lithium-ion batteries with different porosities. The performance of a lithium-ion battery is, to a large extent, determined by the microstructure (i.e., layer thickness and porosity) of its electrodes. Tailoring the microstructure to a specific application is a crucial process in battery development. However, unravelling the complex correlations between microstructure and rate performance using either experiments or simulations is time-consuming and costly. Our approach provides a swift method for predicting the rate capability of battery electrodes by using machine learning on microstructural images of electrode cross-sections. We train multiple models in order to predict the specific capacity based on the batteries’ microstructure and investigate the decisive parts of the microstructure through the use of explainable artificial intelligence (XAI) methods. Our study shows that even comparably small neural network architectures are capable of providing state-of-the-art prediction results. In addition to this, our XAI studies demonstrate that the models are using understandable human features while ignoring present artefacts.
Die Begriffe künstliche Intelligenz und digitaler Zwilling sind prägende Themen der heutigen Zeit. Zwar beeinflussen beide Themen die Entwicklung der Gesellschaft auf verschiedene Arten und Weisen. Dennoch sind sie als Untersuchungsobjekt schlecht definiert und es herrschen etliche unterschiedliche Verständnisse dieser Begriffe.
Der vorliegende Aufsatz nähert sich dem Begriffen künstliche Intelligenz und digitaler Zwilling durch eine Literaturanalyse, um das Verständnis dieser Begriffe in ihrer Nutzung zu schärfen. Im Anschluss der Begriffsbetrachtung wird für jeden der zwei Begriffe betriebliche Anwendungsbereiche aufgezeigt. Folgend den Einzelbetrachtungen werden mögliche Forschungsrichtungen aufgezeigt und diskutiert.