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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.
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.
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.
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.
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.
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.
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.