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.
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.
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.
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.
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.
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
The Institute of Mathematics develops and improves models to describe industrial processes using new algorithms and machine learning.
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.
The Institute of Computer Science is dedicated to improving digital twins and creating human-centred workplaces for the digitalised world of Industry 4.0.
The Institute for Software and Systems Engineering develops artificial intelligence methods for the self-organisation of highly automated systems.
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.