Development Methodology

The increasing dynamism and variety of relationships amongst systems and within systems require new approaches in order for them to be developed. The research area - development methodology - focuses on fundamentals and principles required to tackle future challenges, on the basis of the four interlocking pillars of systems engineering - processes, methods, organization and tools. Furthermore, competences and skills required for future ‘systems engineers’ are being defined to suit future labour markets. Development methodology is also used as an overall framework to connect different competence and research areas of the IME.

Systems Engineering

System engineering (SE) is an interdisciplinary approach for the development of complex socio-technical systems and provides principles and best practices for various development challenges. Recent research activities of the IME have focused on the development of complex powertrain systems. Future forms of mobility pose new challenges for modern powertrain systems. The powertrain system is confronted with an increasing complexity in the development concerning the technical as well as the organizational side. Trend topics such as electrification or autonomous driving require new approaches that enable optimized interaction between the individual systems in the powertrain. This already distinguishes itself and is reinforced by requirements in terms of short development time, increasing diversity of variants and functionality at lower costs. New approaches are needed that unite different disciplines in development and that support the search for the best possible solution considering requirements and use cases.

More recent research activities expand this focus towards the development of drone systems. These highly integrated cyber-physical systems introduce additional challenges regarding system architecture, autonomy, communication, and validation. Systems engineering principles are applied to ensure a consistent development approach, including digital twin concepts and prototype development.

In cooperation with AVL List GmbH, a book project was initiated to highlight the benefits of implementing systems engineering in interdisciplinary development of automotive powertrains. Systems engineering principles are described as well as its application in specific aspects of powertrain development such as fuel cell or e-drive development.

“Systems engineering is an approach, a philosophy for development, that combines well-proven processes, methods, tools, participating engineers (organization) and stakeholders for the development of complex systems. In the scope of technical development, systems engineering is a structured and connected way of thinking and working. Interdisciplinary collaboration and a holistic perspective are of pivotal importance. A discipline-specific perspective is not sufficient to consider all aspects and influences due to the increasing complexity of the systems such as powertrain systems.“ (Bajzek et al. 2020)

Model-based Systems Engineering (MBSE)

Especially model-based systems engineering (MBSE) brings huge potential with it, and relies on a set of models, which are developed over the course of product development. These models are generated by using related methods and describe the system under development regarding its structure, behavior and requirements. One important task is to develop concepts that support to get an overview of used models thus described system aspects. The figure below shows such a concept developed at the IME:

[Translate to Englisch:] © Hick et al. 2019

Another topic of interest is the generation of a “digital master” during the development. The base for the digital master is provided by a consistent and connected model-based development approach. MBSE supports this development approach e.g., by providing system models, the necessary methods and related modeling languages.

After the development phase, the digital twin – a digital representation of a physically existing product - is derived from the digital master. Model-based development is a prerequisite to realize this vision of a digital twin and therefore supports development of new business models and provides opportunities for systems optimization in general.

Artificial Intelligence (AI) in Systems Engineering and Product Development

Artificial Intelligence (AI) is reshaping how complex systems are developed. In systems engineering and mechatronic product development, AI enables data-driven insights, intelligent automation and adaptive support across the entire lifecycle, from early requirements analysis to validation and verification.

At the IME, we investigate how AI approaches such as machine learning, including deep learning based on neural networks, as well as AI agents can actively support engineers in handling complexity, exploring design spaces and improving decision quality. This includes discipline-specific design activities such as CAD, as well as verification and validation activities such as simulation, where AI can support modeling, analysis and evaluation tasks.

Integrating AI into development is not just about adding new tools. It requires rethinking the methodological foundation of engineering. Traditional development approaches are largely deterministic and model-based, while AI systems are data-driven, probabilistic and continuously evolving. This shift challenges established concepts of verification, validation, traceability and responsibility.

Our research therefore focuses on developing a new methodological framework that systematically combines model-based systems engineering with data-driven AI approaches. This includes structured lifecycle concepts for AI components, covering data management, training and monitoring, clear integration into existing processes, and well-defined human–AI collaboration models. Transparency, explainability and trust are central design principles.

The goal is to create a robust and future-oriented development environment in which AI is not an add-on, but an integrated and reliable element of interdisciplinary system development, enabling innovation for complex socio-technical systems in industry and research.

Current Research Activities

Drone systems (digital twin and prototype development)
Artificial Intelligence (AI) and AI-Agents in the context of systems engineering and product development
Engineering education, human-enablement, and workforce development
Engineering automation in interdisciplinary system development
Descriptive modeling approach to support mechanical development
Concepts of a digital twin
Model-based systems engineering (MBSE) and the generation of the digital master
System models and model structure for cyber-physical system development
Evaluation of system modeling languages
Analysis and structuring of development methods
etc.

To top

Contact

Institute of Machine Components and Methods of Development

Inffeldgasse 21b/II
8010 Graz

Tel.: +43 316 873 7361
Fax: +43 316 873 7370

Email: imenoSpam@tugraz.at
Web: www.ime.tugraz.at

Current Publications

Bajzek, M., Krems, D., Kollegger, S., Traußnig, T., Dörr, S., Tatschl, M., Kniewallner, J. and Hick, H. 2025. Accelerated Automotive Battery Development to meet Market Opportunities. INCOSE International Symposium, 35: 760-777. https://doi.org/10.1002/iis2.70007

Kranabitl, P., Faustmann, C., Bajzek, M. et al. A fundamental concept for linking methods, system models, and specific models. Discov Appl Sci 6, 2 (2024). https://doi.org/10.1007/s42452-024-05651-5

Hick, H.; Küpper, K.; Sorger, H.; Systems Engineering for Automotive Powertrain Development; Springer; 2020; ISBN 978-3-319-99628-8;in press;
https://www.springer.com/de/book/9783319996288

Grebe, U.; Hick, H.; Rothbart, M.; von Helmolt, R.; Armengaud, E.; Bajzek, M.; Kranabitl, P.; Challenges for Future Automotive Mobility; In: Hick, H.; Küpper, K.; Sorger, H.; Systems Engineering for Automotive Powertrain Development; Springer; 2020; ISBN 978-3-319-99628-8; in press;
https://www.springer.com/de/book/9783319996288

Bajzek, M.; Fritz, J.; Hick, H.; Systems Engineering Principles; In: Hick, H.; Küpper, K.; Sorger, H.; Systems Engineering for Automotive Powertrain Development; Springer; 2020; ISBN 978-3-319-99628-8; in press;
https://www.springer.com/de/book/9783319996288

Bajzek, M.; Fritz, J.; Hick, H.; Maletz, M.; Faustmann, C.; Stieglbauer, G.; Model-Based Systems Engineering Concepts; In: Hick, H.; Küpper, K.; Sorger, H.; Systems Engineering for Automotive Powertrain Development; Springer; 2020; ISBN 978-3-319-99628-8; in press;
https://www.springer.com/de/book/9783319996288

Fischer, R.; Vorbach, S.; Hick, H.; Systems Engineering Organizational Constraints and Responsibilities; In: Hick, H.; Küpper, K.; Sorger, H.; Systems Engineering for Automotive Powertrain Development; Springer; 2020; ISBN 978-3-319-99628-8; in press;
https://www.springer.com/de/book/9783319996288

Hick, H.; Angel, H.-F.; Kranabitl, P.; Wagner-Skacel, J.; Decision-Making and the Influence of the Human Factor; In: Hick, H.; Küpper, K.; Sorger, H.; Systems Engineering for Automotive Powertrain Development; Springer; 2020; ISBN 978-3-319-99628-8; in press;
https://www.springer.com/de/book/9783319996288

Kranabitl, P.; Bajzek, M.; Atzwanger, M.; Schenk, D.; Hick, H.; Automotive Powertrain Development Process; In: Hick, H.; Küpper, K.; Sorger, H.; Systems Engineering for Automotive Powertrain Development; Springer; 2020; ISBN 978-3-319-99628-8; in press;
https://www.springer.com/de/book/9783319996288

Maletz, M.; Bajzek, M; Hick, H.: Systems Engineering Methods for Automotive Powertrain Development; In: Hick, H.; Küpper, K.; Sorger, H.; Systems Engineering for Automotive Powertrain Development; Springer; 2020; ISBN 978-3-319-99628-8; in press;
https://www.springer.com/de/book/9783319996288

Schöffmann, W.; Sorger, H.; Faustmann, C.; Bajzek, M.: Case Study – Engine System Development; In: Hick, H.; Küpper, K.; Sorger, H.; Systems Engineering for Automotive Powertrain Development; Springer; 2020; ISBN 978-3-319-99628-8; in press;
https://www.springer.com/de/book/9783319996288

Faustmann, C.; Kranabitl, P.; Bajzek, M.; Fritz, J.; Hick, H.; Sorger, H.; Future of Systems Engineering; In: Hick, H.; Küpper, K.; Sorger, H.; Systems Engineering for Automotive Powertrain Development; Springer; 2020; ISBN 978-3-319-99628-8; in press;
https://www.springer.com/de/book/9783319996288

Hick, H.; Bajzek, M.; Kranabitl, P.; Faustmann, C.; Erweitertes Digital-Twin-Konzept unter Berücksichtigung des Entwicklers; 2020; Zeitschrift für wirtchaftlichen Fabrikbetrieb; Jahrg. 115 (2020) Special; in press;
https://doi.org/10.3139/104.112302

Walch, S.; Kranabitl, P.; Bajzek, M.; Wobelka, T.; Hick, H.; Kollegger, S.; Model Based Systems Engineering (MBSE) in der Tribologie – Anwendung von SysML-Tool in der Reib- und Verschleißanalyse von Antriebsstrangkomponente; 2019; Österreichische Tribologische Gesellschaft; Symposium 2019;
https://graz.pure.elsevier.com/de/publications/model-based-systems-engineering-in-der-tribologie-anwendung-eines

Hick, H.; Bajzek, M.; Faustmann, C.; Definition of a system model for model-based development; SN Applied Science; 1, 1074; 2019;
https://doi.org/10.1007/s42452-019-1069-0