Research Project
Wide Range of Topics
Next Generation Engineering
Driving Innovation Through Systems Engineering
True innovation arises where disciplines seamlessly intersect. By applying systems engineering, we establish the methodological foundation for highly iterative and agile product development based on valid models rather than isolated documents. CSE helps you embed these modern development methods deeply within your organization to prevent errors early on and create space for creative solutions.
Breaking Down Silos
Model-Based Development
Traditional departmental silos hinder efficiency in modern product development. Through model-based systems engineering (MBSE), we replace static documents with a central system model as the “single source of truth.” At CSE, we develop methods to seamlessly integrate disciplines such as mechanics, electronics, and software, enabling agile, cross-disciplinary collaboration that makes even the most complex challenges manageable.
Function-oriented Models
Function-oriented models form the foundation of an architecture that focuses on system behavior—even before the physical implementation is defined. At CSE, we use this abstraction to map complex requirements in a discipline-neutral manner and to validate logical relationships early on. This approach enables us to design innovative solutions independent of specific technologies and to ensure the functional safety and efficiency of the subsequent implementation as early as the initial design phase.
SysML
Interdisciplinary Modeling Language
To overcome the communication barriers between mechanical, electronic, and software engineering, we rely on a unified, cross-disciplinary modeling language. At CSE, we use and optimize languages such as SysML, which serve as the “lingua franca” of systems development and unify all technical aspects into a consistent overall model. This is an essential prerequisite for automated communication between different development tools and ensures that all project participants always operate on the basis of a shared, unambiguous understanding of the system.
AI in Engineering
Artificial intelligence (AI) is currently transforming engineering processes across the entire product lifecycle at a rapid pace. In particular, generative AI and large language models (LLMs) enable interaction with complex technical data and system models using natural language. When combined with Model-Based Systems Engineering (MBSE) and the new SysML v2 standard, AI can directly generate and interpret textual system models.
The textual representation of SysML v2 opens the door to AI-driven automation, model generation, requirements analysis, and consistency checks. This significantly lowers the barriers to entry for implementing MBSE in industrial environments. AI in engineering is therefore a key factor in enabling faster development cycles, improved traceability, and more accessible system knowledge.
Generative AI in MBSE
To improve the accessibility and efficiency of MBSE, current research is exploring the use of generative AI as a technical copilot. AI-based assistance systems in MBSE can be categorized by maturity level: from no AI support (Level 0) to fully autonomous AI assistants (Level 5) that independently plan and execute technical tasks. Today’s LLM-based tools already enable Level 1 and 2 applications, such as automated documentation, requirements analysis, or model generation.
Current research is exploring applications such as automated traceability between requirements and system models and the generation of SysML v2 code. Benchmarks such as SysEngBench evaluate LLM performance in key areas of systems engineering, thereby demonstrating the growing capabilities in this field. However, most existing approaches focus on the modeling phase, while AI-assisted model utilization and integration into the lifecycle remain under-researched.
For industry, this means that AI in MBSE is technically feasible and increasingly practical, but still requires structured implementation, domain-specific data strategies, and careful validation to ensure reliability, robustness, and the protection of intellectual property.
Motivation
Sustainable Development
Mastering the circular economy is increasingly becoming a strategic success factor in the manufacturing industry. Against the backdrop of regulatory requirements, growing resource scarcity, and rising customer expectations, companies face the challenge of fundamentally realigning their value creation. Linear product and business models are gradually being replaced by circular approaches to thinking and action.
Circular Lifecycle Engineering
Circular Lifecycle Engineering describes a holistic approach to designing circular value creation systems across the entire product lifecycle—from product development through production and use to reuse, remanufacturing, and recycling. The goal is to develop products, systems, and business models in such a way that resources remain in circulation for as long as possible, value loss is minimized, and both ecological and economic potentials are equally tapped.
Digital Continuity Throughout the Lifecycle
At the core is an integrated lifecycle perspective that links technical, organizational, and economic aspects. Circular product architectures, modular and repair-friendly designs, data-driven lifespan predictions, and end-to-end digital information systems throughout the product lifecycle form key building blocks.
This brings fundamental design questions into focus: What organizational structures and competencies are required to systematically embed circular value creation? What methods, models, and tools support the development and evaluation of circular product concepts? How can lifecycle data be used in a targeted manner to enable informed decisions and realize economically viable circular business models?
