Towards a Holistic and Functional Model-Based Design Method for Mechatronic Cyber-Physical Systems

IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Computing and Information Science in Engineering Pub Date : 2023-01-31 DOI:10.1115/1.4056807
Gregor Hoepfner, I. Nachmann, T. Zerwas, J. Berroth, J. Kohl, C. Guist, Bernhard Rumpe, G. Jacobs
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Abstract

Engineering Cyber-Physical Systems (CPS) is complex and time-consuming due to the heterogeneity of the involved engineering domains and the high number of physical and logical interactions of its subsystems. Model-based Systems Engineering (MSBE) approaches tackle the complexity of developing CPS by formally and explicitly modeling subsystems and their interactions. Newer approaches also integrate domain-specific models and modeling languages to cover different aspects of CPS. However, MBSE approaches are currently not fully applicable for CPS development since they do not integrate formal models for physical and mechanical behavior to an extent that allows to seamlessly link mechanical models to the digital models and reuse them. In this paper, we discuss the challenges arising from the missing integration of physics into MBSE and introduce a model-based methodology capable of integrating physical functions and effects into an MBSE approach on a level where detailed physical effects are considered. Our approach offers a fully virtual, model-based development methodology covering the whole development process for the development of CPS. Evaluating this methodology on a real automotive use case demonstrates benefits regarding virtual development and functional testing of CPS. It shows potentials regarding automated development and continuous integration of the whole CPS including all domains. As an outlook of this paper, we discuss potential further research topics extending our development workflow.
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基于整体功能模型的机电信息物理系统设计方法研究
工程信息物理系统(CPS)由于涉及工程领域的异质性以及子系统之间大量的物理和逻辑交互而复杂且耗时。基于模型的系统工程(MSBE)方法通过正式和显式地对子系统及其交互进行建模来解决开发CPS的复杂性。较新的方法还集成了特定于领域的模型和建模语言,以涵盖CPS的不同方面。然而,MBSE方法目前并不完全适用于CPS开发,因为它们没有将物理和机械行为的正式模型集成到某种程度,从而无法将机械模型无缝地链接到数字模型并重用它们。在本文中,我们讨论了由于缺少将物理集成到MBSE中所带来的挑战,并介绍了一种基于模型的方法,该方法能够在考虑详细物理效应的水平上将物理功能和效果集成到MBSE方法中。我们的方法提供了一个完全虚拟的、基于模型的开发方法,涵盖了CPS开发的整个开发过程。在一个真实的汽车用例上评估这个方法,可以证明CPS的虚拟开发和功能测试的好处。它显示了自动化开发和整个CPS包括所有领域的持续集成的潜力。作为本文的展望,我们讨论了扩展我们的开发工作流的潜在的进一步研究课题。
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来源期刊
CiteScore
6.30
自引率
12.90%
发文量
100
审稿时长
6 months
期刊介绍: The ASME Journal of Computing and Information Science in Engineering (JCISE) publishes articles related to Algorithms, Computational Methods, Computing Infrastructure, Computer-Interpretable Representations, Human-Computer Interfaces, Information Science, and/or System Architectures that aim to improve some aspect of product and system lifecycle (e.g., design, manufacturing, operation, maintenance, disposal, recycling etc.). Applications considered in JCISE manuscripts should be relevant to the mechanical engineering discipline. Papers can be focused on fundamental research leading to new methods, or adaptation of existing methods for new applications. Scope: Advanced Computing Infrastructure; Artificial Intelligence; Big Data and Analytics; Collaborative Design; Computer Aided Design; Computer Aided Engineering; Computer Aided Manufacturing; Computational Foundations for Additive Manufacturing; Computational Foundations for Engineering Optimization; Computational Geometry; Computational Metrology; Computational Synthesis; Conceptual Design; Cybermanufacturing; Cyber Physical Security for Factories; Cyber Physical System Design and Operation; Data-Driven Engineering Applications; Engineering Informatics; Geometric Reasoning; GPU Computing for Design and Manufacturing; Human Computer Interfaces/Interactions; Industrial Internet of Things; Knowledge Engineering; Information Management; Inverse Methods for Engineering Applications; Machine Learning for Engineering Applications; Manufacturing Planning; Manufacturing Automation; Model-based Systems Engineering; Multiphysics Modeling and Simulation; Multiscale Modeling and Simulation; Multidisciplinary Optimization; Physics-Based Simulations; Process Modeling for Engineering Applications; Qualification, Verification and Validation of Computational Models; Symbolic Computing for Engineering Applications; Tolerance Modeling; Topology and Shape Optimization; Virtual and Augmented Reality Environments; Virtual Prototyping
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