Research

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22 pages, 7734 KiB

Open AccessArticle

A Model-Based Approach for the Methodical Development and Configuration of Modular Product Families

by Michael Hanna, Lea-Nadine Wöller, Florian M. Dambietz and Dieter Krause

Systems 2023, 11(9), 449; https://doi.org/10.3390/systems11090449 - 31 Aug 2023

Viewed by 1374

This paper shows how a methodical development and configuration of modular product family concepts and their effects on economic targets can be implemented in SysML. For this purpose, different sources of inconsistency between different methodical tools are highlighted and the need for research [...] Read more.

This paper shows how a methodical development and configuration of modular product family concepts and their effects on economic targets can be implemented in SysML. For this purpose, different sources of inconsistency between different methodical tools are highlighted and the need for research is shown. As a solution approach, a methodical framework is presented, which can be used to implement product development methods for the developing of modular product family modeling by means of Model-Based Systems Engineering (MBSE) in the modeling language SysML. By applying the framework, it is shown on the one hand how a product family of vacuum cleaner robots as a simple example can be modularized in a methodical, model-based manner. On the other hand, a configuration system and an impact model of modular product families are connected with the system model and applied to a product family of laser systems as an industrial use case. This made it clear that the framework can be used to model various methodical topics of product family modeling in a consistent manner, to enable higher-level analyses with the use of MBSE tools. This can reduce errors, decrease effort and increase traceability across different methodical tools. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering: From Design to Practical Systems Engineering)

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24 pages, 3309 KiB

Open AccessArticle

Model Based Systems Engineering with a Docs-as-Code Approach for the SeaLion CubeSat Project

by Kevin Chiu, Sean Marquez and Sharanabasaweshwara Asundi

Systems 2023, 11(7), 320; https://doi.org/10.3390/systems11070320 - 23 Jun 2023

Cited by 1 | Viewed by 1310

Abstract

The SeaLion mission architecture team sought to create a model-based systems engineering approach to assist improving CubeSat success rates as well as for the SeaLion CubeSat project to guide an implementation for the flight software. This is important because university CubeSat teams are [...] Read more.

The SeaLion mission architecture team sought to create a model-based systems engineering approach to assist improving CubeSat success rates as well as for the SeaLion CubeSat project to guide an implementation for the flight software. This is important because university CubeSat teams are growing in number but often have untrained students as their core personnel. This was done using a document-as-code, or docs-as-code, approach. With this the team created tools for the systems architecture with the Mach 30 Modeling Language to create an architecture that is easy to learn and use even for newly admitted team members with little to no training. These tools generate documents via its own code for easy presentation on a local file system without any proprietary software while keeping the model content format-agnostic. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering: From Design to Practical Systems Engineering)

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17 pages, 1030 KiB

Open AccessArticle

Learning MBSE Online: A Tale of Two Professional Cohorts

by Tiantian Li, Leonardo Pollettini Marcos, Wanju Huang, C. Robert Kenley, Kerrie A. Douglas, Emilee A. Madsen and Audeen W. Fentiman

Systems 2023, 11(5), 224; https://doi.org/10.3390/systems11050224 - 28 Apr 2023

Cited by 1 | Viewed by 1099

Abstract

Research has shown that creating an online learning community is vital in Model-Based Systems Engineering (MBSE) training programs and can be facilitated via the Community of Inquiry (CoI) framework. For professional learners, an online learning community is influenced by their organizational affiliations. The [...] Read more.

Research has shown that creating an online learning community is vital in Model-Based Systems Engineering (MBSE) training programs and can be facilitated via the Community of Inquiry (CoI) framework. For professional learners, an online learning community is influenced by their organizational affiliations. The purpose of this research is to explore learning experiences in groups of professional learners with different and homogenous organizational affiliations in an asynchronous online MBSE module. Through the case study methodology, this research examines four sources of data from two cases: Case 1—learners from different organizations (n = 7); and Case 2—overwhelming majority of learners from the same organization (n = 19). Results showed that learners from the same organization reported higher social presence, which, in turn, corresponded to a higher cognitive presence and higher motivation for future MBSE learning. Based on our findings, we recommend that organizations seeking MBSE adoption coordinate with online course providers to create cohorts to participate in the same offerings to facilitate the process of learning community building. We also recommend MBSE course providers facilitate social interaction on multiple communication platforms and create orientation activities for learners from different organizations to promote social presence. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering: From Design to Practical Systems Engineering)

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18 pages, 12589 KiB

Open AccessArticle

Integration of SysML and Virtual Reality Environment: A Ground Based Telescope System Example

by Mostafa Lutfi and Ricardo Valerdi

Systems 2023, 11(4), 189; https://doi.org/10.3390/systems11040189 - 07 Apr 2023

Viewed by 2286

Abstract

In recent years, Model Based Systems Engineering (MBSE) has continued to develop as a standard for designing, managing, and maintaining increasingly complex systems. Unlike the document centric approach, MBSE puts the model at the heart of system design. Among the various MBSE language [...] Read more.

In recent years, Model Based Systems Engineering (MBSE) has continued to develop as a standard for designing, managing, and maintaining increasingly complex systems. Unlike the document centric approach, MBSE puts the model at the heart of system design. Among the various MBSE language development efforts, “Systems Modeling Language (SysML)”, is the most anticipated and broadly utilized in the research and in industrial practice. SysML originated from Unified Modeling Language (UML) and follows the Object-Oriented Systems Engineering Method (OOSEM). SysML diagrams help users create various systems engineering artifacts, including requirements, use cases, operational concepts, system architecture, system behaviors, and parametric analyses of a system model. In the early days of implementation, MBSE languages, including SysML, typically relied on static viewpoints and limited simulation support to depict and analyze a system model. Due the continuous improvement efforts and new implementation approaches by researchers and organizations, SysML has advanced vastly to encompass dynamic viewpoints, in-situ simulation and enable integration with external modeling and simulation (M&S) tools. Virtual Reality (VR) has emerged as a user interactive and immersive visualization technology and can depict reality in a virtual environment at different levels of fidelity. VR can play a crucial role in developing dynamic and interactive viewpoints to improve the MBSE approach. In this research paper, the authors developed and implemented a methodology for integrating SysML and VR, enabling tools to achieve three dimensional viewpoints, an immersive user experience and early design evaluations of the system of interest (SOI). The key components of the methodology being followed in this research paper are the SysML, a VR environment, extracted data and scripting languages. The authors initially developed a SysML for a ground-based telescope system following the four pillars of SysML: Structure, Requirements, Behavior and Parametrics. The SysML diagram components are exported from the model using the velocity template language and then fed into a virtual reality game engine. Then, the SysML diagrams are visualized in the VR environment to enable better comprehension and interaction with users and Digital Twin (DT) technologies. In addition, a VR simulation scenario of space objects is generated based on the input from the SysML, and the simulation result is sent back from the VR tool into the model with the aid of parametric diagram simulation. Hence, by utilizing the developed SysML-VR integration methodology, VR environment scenarios are successfully integrated with the SysML. Finally, the research paper mentions a few limitations of the current implementation and proposes future improvements. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering: From Design to Practical Systems Engineering)

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27 pages, 5455 KiB

Open AccessArticle

Methodology for Certification-Compliant Effect-Chain Modeling

by Iris Gräßler, Dominik Wiechel, Anna-Sophie Koch, Tim Sturm and Thomas Markfelder

Systems 2023, 11(3), 154; https://doi.org/10.3390/systems11030154 - 17 Mar 2023

Cited by 2 | Viewed by 1497

Abstract

The success of engineering complex technical systems is determined by meeting customer requirements and institutional regulations. One example relevant to the automobile industry is the United Nations Economic Commission of Europe (UN ECE), which specifies the homologation of automobile series and requires proof [...] Read more.

The success of engineering complex technical systems is determined by meeting customer requirements and institutional regulations. One example relevant to the automobile industry is the United Nations Economic Commission of Europe (UN ECE), which specifies the homologation of automobile series and requires proof of traceability. The required traceability can be achieved by modeling system artifacts and their relations in a consistent, seamless model—an effect-chain model. Currently, no in-depth methodology exists to support engineers in developing certification-compliant effect-chain models. For this purpose, a new methodology for certification-compliant effect-chain modeling was developed, which includes extensions of an existing method, suitable models, and tools to support engineers in the modeling process. For evaluation purposes, applicability is proven based on the experience of more than 300 workshops at an automotive OEM and an automotive supplier. The following case example is chosen to demonstrate applicability: the development of a window lifter that has to meet the demands of UN ECE Regulations R156 and R21. Results indicate multiple benefits in supporting engineers with the certification-compliant modeling of effect chains. Three benefits are goal-oriented modeling to reduce the necessary modeling capacity, increasing model quality by applying information quality criteria, and the potential to reduce costs through automatable effect-chain analyses for technical changes. Further, companies in the automotive and other industries will benefit from increased modeling capabilities that can be used for architecture modeling and to comply with other regulations such as ASPICE or ISO 26262. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering: From Design to Practical Systems Engineering)

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28 pages, 8970 KiB

Open AccessArticle

Optimizing Ultra-High Vacuum Control in Electron Storage Rings Using Fuzzy Control and Estimation of Pumping Speed by Neural Networks with Molflow+

by Soontaree Seangsri, Thanasak Wanglomklang, Nopparut Khaewnak, Nattawat Yachum and Jiraphon Srisertpol

Systems 2023, 11(3), 116; https://doi.org/10.3390/systems11030116 - 23 Feb 2023

Viewed by 2497

Abstract

This paper presents the design of a fuzzy-controller-based ultra-high vacuum pressure control system and its performance evaluation for a sputter-ion vacuum pump used in the electron storage ring at the Synchrotron Light Research Institute (Public Organization) in Thailand. The production of synchrotron light [...] Read more.

This paper presents the design of a fuzzy-controller-based ultra-high vacuum pressure control system and its performance evaluation for a sputter-ion vacuum pump used in the electron storage ring at the Synchrotron Light Research Institute (Public Organization) in Thailand. The production of synchrotron light requires advanced vacuum technology to maintain stability and prevent interference of electrons in an ultra-high vacuum pressure environment of about 10⁻⁹ Torr. The presence of heat and gas rupture from the pipe wall can affect the quality of the light in that area. The institute currently uses a sputter-ion vacuum pump which is costly and requires significant effort to quickly reduce pressure increases in the area. Maintaining stable vacuum pressure throughout electron motion is essential in order to ensure the quality of the light. This research demonstrates a procedure for evaluating the performance of a sputter-ion vacuum pump using a mathematical model generated by a neural network and Molflow+ software. The model is used to estimate the pumping speed of the vacuum pump and to design a fuzzy control system for the ultra-high vacuum system. The study also includes a leakage rate check for the vacuum system. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering: From Design to Practical Systems Engineering)

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24 pages, 5337 KiB

Open AccessArticle

Proposing a Small-Scale Digital Twin Implementation Framework for Manufacturing from a Systems Perspective

by Jonatan H. Loaiza, Robert J. Cloutier and Kari Lippert

Systems 2023, 11(1), 41; https://doi.org/10.3390/systems11010041 - 11 Jan 2023

Cited by 4 | Viewed by 3238

Abstract

Due to the fourth industrial revolution, manufacturing companies are looking to implement digital twins in their factories to be more competitive. However, the implementation of digital twins in manufacturing systems is a complex task. Factories need a framework that can guide them in [...] Read more.

Due to the fourth industrial revolution, manufacturing companies are looking to implement digital twins in their factories to be more competitive. However, the implementation of digital twins in manufacturing systems is a complex task. Factories need a framework that can guide them in the development of digital twins. Hence, this article proposes a small-scale digital twin implementation framework for manufacturing systems. To build this framework, the authors gathered several concepts from the literature and designed a digital twin subsystem model using a model-based systems engineering (MBSE) approach and the systems engineering “Vee” model. The systems modelling defines the digital twin components, functionalities, and structure. The authors distribute most of these concepts throughout the framework configuration and some concepts next to this general configuration. This configuration presents three spaces: physical, virtual, and information. The physical space presents a physical layer and a perception layer. The information space has a single layer called middleware. Finally, the virtual space presents two layers: application and model. In addition to these layers, this framework includes other concepts such as digital thread, data, ontology, and enabling technologies. This framework could help researchers and practitioners to learn more about digital twins and apply it to different domains. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering: From Design to Practical Systems Engineering)

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21 pages, 8769 KiB

Open AccessArticle

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

by Niamat Ullah Ibne Hossain, Mostafa Lutfi, Ifaz Ahmed, Aditya Akundi and Daniel Cobb

Systems 2022, 10(6), 264; https://doi.org/10.3390/systems10060264 - 19 Dec 2022

Cited by 3 | Viewed by 5897

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering: From Design to Practical Systems Engineering)

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17 pages, 2870 KiB

Open AccessArticle

Automated Identification of Valid Model Networks Using Model-Based Systems Engineering

by Julius Moritz Berges, Kathrin Spütz, Georg Jacobs, Julia Kowalski, Thilo Zerwas, Jörg Berroth and Christian Konrad

Systems 2022, 10(6), 250; https://doi.org/10.3390/systems10060250 - 09 Dec 2022

Cited by 5 | Viewed by 1823

Abstract

To handle increasing complexity in product development, model-based systems engineering (MBSE) approaches are well suited, in which the technical system is represented in a system model. To efficiently test requirements, domain models are integrated into the system model. For each purpose (e.g., battery [...] Read more.

To handle increasing complexity in product development, model-based systems engineering (MBSE) approaches are well suited, in which the technical system is represented in a system model. To efficiently test requirements, domain models are integrated into the system model. For each purpose (e.g., battery lifetime calculation), there are typically several models at several fidelity levels. Since the model signatures (i.e., necessary inputs for the models and their outputs) differ depending on the fidelity level, not all models can be used in any development phase. In addition, due to the different model signatures, not all models can be combined arbitrarily to model networks. Currently, valid model networks in system models must be determined in a time-consuming, manual process. Therefore, this paper presents an approach that automates this task via the implementation of an algorithm that analyzes a system model and the model signatures and automatically returns all valid model networks. When input parameters, models or their signatures change, the algorithm updates automatically, and the user receives the valid model network without any manual effort. The approach is demonstrated with the running example of battery system development. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering: From Design to Practical Systems Engineering)

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20 pages, 3911 KiB

Open AccessArticle

Impact of Reliability in Conceptual Design—An Illustrative Trade-Off Analysis

by Tevari Barker, Gregory S. Parnell, Edward Pohl, Eric Specking, Simon R. Goerger and Randy K. Buchanan

Systems 2022, 10(6), 227; https://doi.org/10.3390/systems10060227 - 21 Nov 2022

Cited by 1 | Viewed by 1702

Abstract

System reliability is treated as a parameter and not modeled in the early concept design stages. We illustrate a reliability model for system reliability in early concept design using knowledge from similar systems, technology readiness levels (TRL), and functional analysis methods using an [...] Read more.

System reliability is treated as a parameter and not modeled in the early concept design stages. We illustrate a reliability model for system reliability in early concept design using knowledge from similar systems, technology readiness levels (TRL), and functional analysis methods using an unmanned ground vehicle. We integrate the reliability model with performance and cost models to demonstrate the impact of reliability in early concept design. The resultant tradespace comparison with and without early reliability assessment illustrates that reliability modeling can identify infeasible solutions in early system design. This will allow system designers to focus development on the most promising concept designs. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering: From Design to Practical Systems Engineering)

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Review

Jump to: Research

20 pages, 4215 KiB

Open AccessReview

Endoscopy Lifetime Systems Architecture: Scoping Out the Past to Diagnose the Future Technology

by Craig M. Browning, Robert Cloutier, Thomas C. Rich and Silas J. Leavesley

Systems 2022, 10(5), 189; https://doi.org/10.3390/systems10050189 - 14 Oct 2022

Viewed by 2005

Abstract

Systems engineering captures the desires and needs of the customer to conceptualize a system from the overall goal down to the small details prior to any physical development. While many systems projects tend to be large and complicated (i.e., cloud-based infrastructure, long-term space [...] Read more.

Systems engineering captures the desires and needs of the customer to conceptualize a system from the overall goal down to the small details prior to any physical development. While many systems projects tend to be large and complicated (i.e., cloud-based infrastructure, long-term space travel shuttles, missile defense systems), systems engineering can also be applied to smaller, complex systems. Here, the system of interest is the endoscope, a standard biomedical screening device used in laparoscopic surgery, screening of upper and lower gastrointestinal tracts, and inspection of the upper airway. Often, endoscopic inspection is used to identify pre-cancerous and cancerous tissues, and hence, a requirement for endoscopic systems is the ability to provide images with high contrast between areas of normal tissue and neoplasia (early-stage abnormal tissue growth). For this manuscript, the endoscope was reviewed for all the technological advancements thus far to theorize what the next version of the system could be in order to provide improved detection capabilities. Endoscopic technology was decomposed into categories, using systems architecture and systems thinking, to visualize the improvements throughout the system’s lifetime from the original to current state-of-the-art. Results from this review were used to identify trends in subsystems and components to estimate the theoretical performance maxima for different subsystems as well as areas for further development. The subsystem analysis indicated that future endoscope systems will focus on more complex imaging and higher computational requirements that will provide improved contrast in order to have higher accuracy in optical diagnoses of early, abnormal tissue growth. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering: From Design to Practical Systems Engineering)

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