Erzeugung von SysML-Stereotypen zur Beschreibung logischer Systemarchitekturen im Model-based Systems Engineering

Aktuelle Systementwicklungen erfordern disziplinspezifische und disziplinübergreifende Modellierungstechniken. Für disziplinübergreifende Modellierungstechniken wird häufig die Systems Modeling Language (SysML) genutzt. Da es sich bei der SysML um eine universell einsetzbare Modellierungssprache handelt, ist diese für konkrete Entwicklungssituationen oft sehr abstrakt. Dieser Beitrag erläutert die gezielte Erweiterung der SysML durch Stereotypen und wendet diese für die Erstellung heterogener Produktmodelle an. Außerdem werden zukünftige Forschungsarbeiten vorgestellt.Current system developments require both discipline-specific and cross-domain modelling techniques. Cross-domain modelling techniques preferably use the Systems Modeling Language (SysML). Since SysML is a universally applicable modelling language, it is often very abstract for specific development situations. This article explains the targeted extension of SysML using SysML element stereotypes and their application within heterogeneous product models. Additionally, future research fields will be presented

In-plant logistics systems modeling with SysML

Up till now Systems Modeling Language (SysML) has mostly been used to model physical systems of interest. This paper shows how SysML can also be used to represent an abstract model. In this application a mathematical cost model is represented using the SysML plugin for the software MagicDraw. ParaMagic, a plugin in MagicDraw supplementary to SysML, links to Mathematica to solve the model. SysML is a formal language and offers a very intuitive graphical representation. It is therefore a useful medium to create a domain specific language for a field of knowledge. The comprehensiveness of the language, which makes it possible to incorporate specification, analysis, design, verification, and validation of systems, makes it a very valuable tool for collaboration on large projects

Reliability Analysis of Complex NASA Systems with Model-Based Engineering

The emergence of model-based engineering, with Model- Based Systems Engineering (MBSE) leading the way, is transforming design and analysis methodologies. The recognized benefits to systems development include moving from document-centric information systems and document-centric project communication to a model-centric environment in which control of design changes in the life cycles is facilitated. In addition, a single source of truth about the system, that is up-to-date in all respects of the design, becomes the authoritative source of data and information about the system. This promotes consistency and efficiency in regard to integration of the system elements as the design emerges and thereby may further optimize the design. Therefore Reliability Engineers (REs) supporting NASA missions must be integrated into model-based engineering to ensure the outputs of their analyses are relevant and value-needed to the design, development, and operational processes for failure risks assessment and communication

Model-Driven Engineering Method to Support the Formalization of Machine Learning using SysML

Methods: This work introduces a method supporting the collaborative definition of machine learning tasks by leveraging model-based engineering in the formalization of the systems modeling language SysML. The method supports the identification and integration of various data sources, the required definition of semantic connections between data attributes, and the definition of data processing steps within the machine learning support. Results: By consolidating the knowledge of domain and machine learning experts, a powerful tool to describe machine learning tasks by formalizing knowledge using the systems modeling language SysML is introduced. The method is evaluated based on two use cases, i.e., a smart weather system that allows to predict weather forecasts based on sensor data, and a waste prevention case for 3D printer filament that cancels the printing if the intended result cannot be achieved (image processing). Further, a user study is conducted to gather insights of potential users regarding perceived workload and usability of the elaborated method. Conclusion: Integrating machine learning-specific properties in systems engineering techniques allows non-data scientists to understand formalized knowledge and define specific aspects of a machine learning problem, document knowledge on the data, and to further support data scientists to use the formalized knowledge as input for an implementation using (semi-) automatic code generation. In this respect, this work contributes by consolidating knowledge from various domains and therefore, fosters the integration of machine learning in industry by involving several stakeholders.Comment: 43 pages, 24 figure, 3 table

Modeling of system knowledge for efficient agile manufacturing : tool evaluation, selection and implementation scenario in SMEs

In the manufacturing world, knowledge is fundamental in order to achieve effective and efficient real time decision making. In order to make manufacturing system knowledge available to the decision maker it has to be first captured and then modelled. Therefore tools that provide a suitable means for capturing and representation of manufacturing system knowledge are required in several types of industrial sectors and types of company’s (large, SME). A literature review about best practice for capturing requirements for simulation development and system knowledge modeling has been conducted. The aim of this study was to select the best tool for manufacturing system knowledge modelling in an open-source environment. In order to select this tool, different criteria were selected, based on which several tools were analyzed and rated. An exemplary use case was then developed using the selected tool, Systems Modeling Language (SysML). Therefore, the best practice has been studied, evaluated, selected and then applied to two industrial use cases by the use of a selected opens source tool.peer-reviewe

Model-Based Systems Engineering Pilot Program at NASA Langley

NASA Langley Research Center conducted a pilot program to evaluate the benefits of using a Model-Based Systems Engineering (MBSE) approach during the early phase of the Materials International Space Station Experiment-X (MISSE-X) project. The goal of the pilot was to leverage MBSE tools and methods, including the Systems Modeling Language (SysML), to understand the net gain of utilizing this approach on a moderate size flight project. The System Requirements Review (SRR) success criteria were used to guide the work products desired from the pilot. This paper discusses the pilot project implementation, provides SysML model examples, identifies lessons learned, and describes plans for further use on MBSE on MISSE-X

A Review on Application of Model Based Systems Engineering to Manufacturing and Production Engineering Systems

Increasing complexity in today’s manufacturing and production industry due to the need for higher flexibility and competitiveness is leading to inconsistencies in the iterative exchange loops of the system design process. To address these complexities and inconsistencies, an ongoing industry trend for organizations to make a transition from document-centric principles and applications to being model-centric is observed. In this paper, a literature review is presented highlighting the current need for an industry-wide transition from document-centric systems engineering to Model-Based Systems Engineering (MBSE). Further, investigating the tools and languages used by the researchers for facilitating the transition to and the integration of MBSE approach, we identify the most commonly used tools and languages to highlight the applicability of MBSE in the manufacturing and production industry

Modeling and Analysis of Unmanned Aerial Vehicle System Leveraging Systems Modeling Language (SysML)

The use of unmanned aerial vehicles (UAVs) has seen a significant increase over time in several industries such as defense, healthcare, and agriculture to name a few. Their affordability has made it possible for industries to venture and invest in UAVs for both research and commercial purposes. In spite of their recent popularity; there remain a number of difficulties in the design representation of UAVs, including low image analysis, high cost, and time consumption. In addition, it is challenging to represent systems of systems that require multiple UAVs to work in cooperation, sharing resources, and complementing other assets on the ground or in the air. As a means of compensating for these difficulties; in this study; we use a model-based systems engineering (MBSE) approach, in which standardized diagrams are used to model and design different systems and subsystems of UAVs. SysML is widely used to support the design and analysis of many different kinds of systems and ensures consistency between the design of the system and its documentation through the use of an object-oriented model. In addition, SysML supports the modeling of both hardware and software, which will ease the representation of both the system’s architecture and flow of information. The following paper will follow the Magic Grid methodology to model a UAV system across the SysML four pillars and integration of SysML model with external script-based simulation tools, namely, MATLAB and OpenMDAO. These pillars are expressed within standard diagram views to describe the structural, behavior, requirements, and parametric aspect of the UAV. Finally, the paper will demonstrate how to utilize the simulation capability of the SysML model to verify a functional requirement