Pushing digital process optimization
Chemnitz University of Technology develops learning algorithm for improved car body manufacturing in BMBF-funded project
The digitization of production is currently one of the most important fields of action in the area of auto body production and its related technologies, so that Germany’s businesses in which this sector is a central branch can secure future growth and employment.
The new research project „Machine Learning for the Prognosis of Process Parameters and Component Quality in Automotive Body Manufacturing“ (ML@Karoprod) is funded by the Federal Ministry of Education and Research (BMBF). Partners in the project include the Professorship of Artificial Intelligence (Prof. Dr. Fred Hamker), the Fraunhofer Institute for Machine Tools and Forming Technology IWU in Dresden (Dr. Mathias Jäckel) as well as Scale GmbH (Dr. Ingolf Lepenies). The project focuses on model development and the application of machine learning (ML) techniques to accelerate the initial planning and serial setup in car body manufacturing. Chemnitz University of Technology will receive € 257 000 of the total funding of roughly € 1.2 million.
The Chemnitz University of Technology schedule follows three main phases: In the first phase, a forward model is trained in order to predict the consequences of an action with unsupervised learning. In the second phase, this trained model will be used to accelerate the model-based Deep Reinforcement Learning Algorithm. Its task will be to find a sequence of production parameters that optimizes the quality of the final product. In the third phase, the developed algorithm is tested on the real production system, in which its robustness against missing sensory information will be tested and evaluated.
For further information, please contact Prof. Dr. Fred Hamker, Chair of Artificial Intelligence, Tel. +49 371 531-37875, E-mail email@example.com
(Article: Matthias Fejes / Translation: Jeffrey Karnitz)
Prof. Dr. Fred Hamker, Chair of Artificial Intelligence, Tel. +49 371 531-37875, E-mail firstname.lastname@example.org