Machine Learning for Products and Production Systems
Thanks to machine learning (ML), knowledge can be generated from data to create added value at all levels of business processes. Products such as mobile platforms, robots or vehicles are utilising data to optimise their behaviour.
However, production systems are also drawing on it more and more often in order to enable them to react to new market developments and client needs in a more agile manner, and to produce the best products using available resources. In the process, using ML methods close to the source of data is especially promising. The project objective is to make ML available for intelligent products and production methods.
To that end, state-of-the-art ML methods are expected to be integrated into products and production chains. The project is also about increasing business awareness of how to utilise ML for agile business models. The main areas of focus are hybrid learning methods, integration of expert knowledge, data interpretability, learning from data streams, as well as Cognitive Edge Computing. ML methods are considered across applications, using three industrial use cases: State monitoring, process optimisation and product quality improvement. Results and methods are made available to companies on a ML platform. For instance, this platform consists of reference implementations, data preparation and data visualisation methods as well as application knowledge on typical processes when using ML methods.