Engineering AI methods for manufacturing processes

2011 - #

© Heinle, Wischer und Partner, Freie Architekten.

Impelled by digitalization and rapid advances in machine learning, data-driven methods are becoming more important for manufacturing. Often replacing or augmenting modelling and knowledge-based approaches, these data-driven methods serve many purposes. They can help optimize individual steps in the workflow and control complex processes to minimize waste and maximize productivity. They can detect anomalies, enable predictive maintenance and optimize the logistics of complex supply chains. Computers can use sensors and actuators to learn how process parameters and outcomes are related. This enables engineers to develop and roll out ‘immature’
processes – that is, production processes that are not yet fully understood in terms of the physics or engineering.

Fraunhofer IOSB in Karlsruhe and Lemgo has realized artificial intelligence (AI) applications like this in many projects with industry. Seeking to take an even more systematic approach with an ideal infrastructure, we joined forces with Fraunhofer ICT and the Karlsruhe Institute of Technology to build the Karlsruhe Research Factory. Its mission is not limited to optimizing production steps and processes, but will go beyond to establish a comprehensive methodology for applying AI to industrial manufacturing. Our aim is to develop an engineering-based approach to reliably predict the operational behavior of manufacturing processes even in the planning phase.

More information can be found in the press release of 25.7.2019 »Forschungsfabrik: Testzone für die agile Produktion - Grundsteinlegung der Karlsruher Forschungsfabrik befeuert Vision von der selbstlernenden Fertigung«