Junior research groups and junior professorships
The unique combination of the competencies of the university and research locations Stuttgart and Karlsruhe will strengthen scientific excellence and the education of young scientists in Baden-Wuerttemberg.
For this the InnovationCampus Future Mobility (ICM) is establishing several junior research groups and junior professorships at both universities.
Model-based development in production automation
Jun.-Prof. Dr. rer. nat. habil. Andreas Wortmann (ISW, University of Stuttgart)
The goal of the junior research group is to develop novel concepts, methods and tools for the systematic development and operation of the cyber-physical production systems of the future. Part of this is the exploration of abstraction and automation
- in modeling to help domain experts participate in value creation through software,
- in semantically based software architectures that integrate domain-specific models at development time,
- for digital twins that optimize system operation with modeled domain knowledge.
Risk analysis of cyber-physical production systems
Jun.-Prof. Dr.-Ing. Andrey Morozov (IAS, University of Stuttgart)
The research interest of Jun.-Prof. Morozov lies at the intersection of three domains, namely, (i) Networked Automation Systems (NAS), (ii) Dependability, and (iii) Artificial Intelligence (AI). Modern NAS is a particular case of Cyber-Physical Systems (CPS) with the focus on the cooperation of heterogeneous industrial robotic systems. Accurate assessment of reliability, safety, and resilience is essential for NAS because of the high cost of downtime and strict safety requirements. However, the analytical capabilities of dependability evaluation methods, which are currently applied in the industry, are far behind the technical level of the systems in question. These methods cannot adequately describe sophisticated failure scenarios of highly dynamic and intelligent NAS.
Besides that, future NAS will include more and more AI components. However, the reliability and safety analysis of AI is an entirely open question at the moment. An inevitable revolution in the dependability methods is expected in the next years. So, the main goal is to build a strong research team capable of taking a leading role in the development of the next generation of dependability analysis methods for modern and future NAS.
Highly versatile, area and space mobile system for production
Jun.-Prof. Dr. -Ing. Rania Rayyes (IFL, KIT)
Using pre-programmed robots restricts the robot's adaptability, flexibility and even applicability. Additionally, most complex tasks in production and material handling are still performed manually, which is inefficient, prone to errors, and very costly in time. Robot learning opens up new application fields that could not be automated with conventional and traditional techniques.
The professorship focuses on developing novel machine learning methods and AI systems to introduce a new generation of smart, versatile, and adaptive autonomous robotic systems for flexible material handling and manufacturing tasks. It is intended to strengthen the novel research in flexible production and manufacturing at Institute for Material Handling and Logistics (IFL) and innovation for new future at ICM.
Modeling and Analysis in Mobility Software Engineering
Jun.-Prof. Dr. rer. nat. Maike Schwammberger
The research group focuses on logical and diagram-based models and analysis methods in the field of autonomous driving. The central research question is: Which requirements should an autonomous vehicle fulfill before it is allowed to share the roads of this world with people and how can these requirements be ensured? For this purpose, system properties such as safety (collision-freedom), fairness, explainability and moral reasoning of autonomous agents are defined and analyzed.
Research and development of key technologies for a universal machine
Dr.-Ing. Michael Jarwitz (IFSW, University of Stuttgart)
New mobility solutions, as well as developments in production technology itself, offer great potential for reducing global CO2 emissions and for more sustainability. This requires correspondingly versatile and universally applicable manufacturing systems.
The junior research group is working on the research and development of the key technologies for a universal machine for a fully digitized production with a highly versatile manufacturing technology for the location-independent, highly efficient production of functionalized components for the emission-free mobility of tomorrow.
Sensor-based development of H2 fuel cells (SensE2B)
Dr.-Ing. Jan Haußmann (IPEK, KIT)
Polymer Electrolyte Fuel Cells (PEFC) are a promising technology for avoiding local emissions and reducing greenhouse gases. For mobile applications, a high power density and a long lifetime are necessary. For this purpose, it is necessary to detect critical conditions in the fuel cell and to avoid them by an adapted operating strategy.
Within this junior research group a sensor-based concept is to be developed that links the cell, stack and system levels and thus enables the development of a highly efficient fuel cell system with a long lifetime.
Digital twins as grey box models for manufacturing engineering
Dr.-Ing. Christoph Hinze (ISW, University of Stuttgart)
The central element of the junior research group "Digital Twins as Grey-Box Models for Manufacturing Engineering" is the model-based representation of the dynamic behavior in the digital twin for the design, control and optimization of manufacturing systems. For this purpose, various identification procedures and methods from machine learning are applied.
Dynamic quality control for the sustainable production of mobility solutions
Dr.-Ing. Florian Stamer (wbk, KIT)
Quality is a central aspect for the economic success of remanufactured mobility products. The aim of the junior research group is the intelligent quality control of production processes for the economic processing of remanufactured products. This includes both the individual production of components and adaptive pairing in assembly.