Insight最新文献

Performing verification and validation (V&V) as early as possible has become a critical challenge for companies. Requirements and their associated models, elaborated during the engineering phase, often present quality issues that must be systematically verified. This paper introduces a conceptual model of requirements, together with writing patterns specifically designed for system requirements. These contributions provide a structured approach to writing system requirements and will form a foundation for future work on requirements modeling and verification.

Conversational generative artificial intelligence (CGenAI) offers new opportunities to support systems engineering practices but raises numerous challenges regarding human-AI teaming design. This ongoing project is exploring the design of human-CGenAI interaction (H-CGenAI.I) within socio-technical systems and proposes a modelling approach grounded in model-based systems engineering (MBSE). A scenario-driven methodology and SysML profile will be introduced to capture and formalise interactive patterns. The study also intends to develop an evaluation framework based on heuristics and practitioner feedback. This research aims to foster a human-centred integration of H-CGenAI.I into systems engineering analysis, enhancing traceability, collaboration, and reliability in the engineering of socio-technical systems.

The digital thread concerns the integration of engineering data across a system's lifecycle and has emerged as a cornerstone of modern digital engineering. One promising application is in engineering education, where it can expose students to authentic, connected workflows across multiple domains. The digital engineering factory (DEF), developed at the University of Arizona, is a web-based platform designed to support systems and software engineering students by providing integrated access to tools for project management, requirements, modeling, analysis, verification, and test planning. These tools are linked through Violet, a central hub that aggregates data into a comprehensive, semantically structured database. Using the ontological modeling language (OML) and the University of Arizona ontology stack (UAOS), the DEF supports reasoning, validation, and querying. These capabilities enable dynamic dashboards to guide students and assist instructors with assessment. In a classroom deployment, students worked in role-defined teams using the DEF to manage their project data and participate in model-based design and review. The DEF provided students with a clear view of the full engineering lifecycle and enabled automated grading based on traceable, semantically validated data. Ongoing development is focused on improving automated workflows and enhancing the student and instructor experience.

Digital engineering (DE) promises faster, more reliable product development—but only when its benefits are tangible to practitioners. This concise 5-page paper distills a years-long applied research effort at Dassault Systèmes into a portable showcase: a five-axis Arduino-based robotic arm modelled, simulated, manufactured, and verified through a single digital thread. Using SysML-based model-based systems engineering (MBSE) (MagicGrid method), Modelica multiphysics, robotic simulation, FMI co-simulation, and MQTT-enabled hardware-in-the-loop, we demonstrate how requirements trace directly to architecture, mechanics, electronics and code. The result is a replicable template for universities and industry teams seeking to adopt digital engineering (DE) with minimal cost and maximum pedagogical impact.

The decision analysis data model (DADM) formalizes an architecture for decision making in engineering. This article describes a DADM implementation called the armaments interoperability and integration framework (IoIF). IoIF is a configurable software framework that supports engineering analysis and decision-making. IoIF uses linked data to facilitate data interoperability across mission, system, and discipline-specific models. At its core is a semantic representation of a system and a formalized model of the system analysis process. This can be applied to decision analysis as described by DADM. Using IoIF and linked front ends, a user can incorporate engineering analyses into analytic workflows to aid in decision making.

Decision management is the systems engineering life cycle process for making program/system decisions. The purpose of the decision management process is: “… to provide a structured, analytical framework for objectively identifying, characterizing, and evaluating a set of alternatives for a decision at any point in the life cycle and select the most beneficial course of action. Systems engineers and systems analysts need to inform decisions in a digital engineering environment. This paper describes a decision analysis data model (DADM) developed in model-based systems engineering software to provide the process, methods, models, and data to support decision management. DADM can support digital engineering for waterfall, spiral, and agile development processes. This paper describes the decision management processes and provides the definition of the data elements. DADM is based on ISO/IEC/IEEE 15288, the INCOSE Systems Engineering Handbook, the SE Body of Knowledge, the Data Management Body of Knowledge, systems engineering textbooks, and journal articles. The DADM was developed to establish a decision management process and data definitions that organizations and programs can tailor for their system life cycles and processes. The DADM can also be used to assess organizational processes and decision quality.

In the animal kingdom, survival depends on knowledge transfer. But the solitary octopus is an exception. Brilliant and adaptable, each octopus dies with its hard-won knowledge. Engineering projects often face the same plight: insights remain locked in individual models and documents, never reaching others who could benefit subsequently. As a result, teams repeatedly reinvent the wheel–recreating models, wasting time, and compounding technical debt. To break this cycle, digital engineering needs curated, discoverable assets. This article outlines recent efforts to update the OMG Reusable Asset Specification (RAS 3.0) to enable better discovery through structured metadata, searchable asset catalogs, and curation services, and accelerating reuse, collaboration, and scalability.

Organizations seeking to transition to digital engineering (DE) practice face a significant challenge in their need to understand what capabilities are necessary to achieve their DE goals and how these capabilities should mature over time. Further, an organization's DE goals are driven by their specific organizational responsibilities and gaps they may have across multiple dimensions: DE environments and infrastructure, workforce development and skills, workflows, DE practice, and time. The DE capability maturity and assessment framework (CMAF) helps an organization identify desired capabilities, required maturity levels on a specified timeline, and any gaps across all five DE capability areas. The DE CMAF was developed for and through support of the US Army, intended generally for US Department of Defense (DoD) organizations, and used thus far by the US Army, Navy, and Air Force. However, its design and content are extensible and applicable to all organizations.

With the increasing complexity of commercial aircraft and the rapidly changing market demands, the system engineering development pattern extensively adopted by aircraft OEM has evolved from the traditional document-based systems engineering (DBSE) to model-based systems engineering (MBSE) and pattern-based systems engineering (PBSE). MBSE employs models to describe products, while PBSE builds upon MBSE by utilizing engineering patterns, which are validated in advance, to enhance the efficiency and quality of data production in both MBSE and DBSE. However, during PBSE engineering practices, we have observed certain challenges, such as the barriers to initializing product S* models and the low efficiency in generating instances. Artificial intelligence for systems engineering (AI4SE) is an emerging concept aimed at creating a more efficient and user-friendly systems engineering implementation environment through the integration of artificial intelligence (AI), machine learning (ML), and related technologies. This paper explores the application of AI4SE in real-world engineering projects by leveraging large language models (LLMs) to develop a methodology that reduces the deployment threshold of PBSE for enterprises and enhances the efficiency of instance generation.