Our Research
Research Focus
Within the framework of the AI Production Network, various thematic focal points are to be researched by the participating partners. These focal points represent key elements of the overarching vision of AI-based production. The research foci of the AI Production Network can be divided into the two areas of "Materials & Production Technology" (for which the Faculty of Mathematics and Natural Sciences is responsible) and "Digitalisation & Self-Organisation" (for which the Faculty of Applied Computer Science is responsible).
Materials and Production Technology
The area of Materials & Production Technology involves the use of AI for material and process-related topics. This includes a holistic view of production processes and value creation networks, starting with materials development and design. Through the use of artificial intelligence, processes are to be designed resiliently and optimised based on data.
Digitalisation and Self-Organisation
The area of digitalisation & self-organisation focuses on artificial intelligence methods for the digitalisation of materials, products, processes and production networks. In addition to the concept of the digital twin, methods for the self-organisation of processes and the role of humans in AI-based production will be researched.
Interdisciplinary Modelling
An interdisciplinary team explores methods, technologies and tools ranging from multi-physical simulation to data-based and hybrid models supported by machine learning.
Resilient Materials Technologies and Value Creation Networks
In order to be able to produce the materials of the future reproducibly and in consistent quality across locations, resistant and adaptable technical systems are necessary.
Generative Design Methods and Materials Development
Artificial intelligence methods are used to utilise the potential of generative design methods for complex materials. These help to understand complex interactions based on process and characterisation data.
Adaptive Manufacturing Processes and Closed-Loop Control
Based on data from process or condition monitoring systems, manufacturing processes are to be optimised using machine learning methods and, in addition, self-adaptively controlled in real time.
Digital Twins for Product, Material, Processes, and Production Network
For the implementation of digital technologies, a data-based description of objects, resources and processes is necessary. Digital twins form the digital representation of the physical component, plant and process status.
Human Centered Production Technologies
In an AI-supported production environment, the role of humans changes. The sovereignty of humans should be secured and their expertise as well as their potential for optimising complex processes should be used in order to optimally cooperate with production technologies.
Self-Organizing Process Route Planning
Networked, modular manufacturing cells form the infrastructure of a production network. For optimal utilisation, plants should configure themselves and autonomous systems should take over the planning of process routes.
Participating Institutions of the University of Augsburg
Institute of Mathematics
The Institute of Mathematics develops and improves models to describe industrial processes using new algorithms and machine learning.
Institute of Materials Resource Management
Using AI to utilise the potential of new sustainable materials and to intelligently monitor processes - these are the solutions the Institute of Materials Resource Management is working on.
Institute of Computer Science
The Institute of Computer Science is dedicated to improving digital twins and creating human-centred workplaces for the digitalised world of Industry 4.0.
Institute for Software & Systems Engineering
The Institute for Software and Systems Engineering develops artificial intelligence methods for the self-organisation of highly automated systems.
Application Center for Materials and Environmental Research
With its many years of experience in knowledge transfer, the Application Center for Materials and Environmental Research makes the university's know-how available to society and industry as a whole.