Lukas Wehinger

The previous three parts of our series focused on the technologies “under the hood” of DEEP, the Fraunhofer IKS machine learning toolchain. Now, we take a look at the “big picture” in form of the process steps of the DEEP procedure – how DEEP can be used to get a grip on the problems associated with the use of machine learning (ML) for future flexible quality inspection.

DEEP, the machine learning toolchain of Fraunhofer IKS, enables the automation of quality control in production environments, even if the requirements are complex. The technologies FAST and Robuscope, likewise developed by Fraunhofer IKS, are essential components of this solution.

Machine learning (ML) is considered a promising technology for the automation of quality control in production environments, even if the requirements are complex. However, ML approaches require sufficiently large datasets for training of the system, which are often unavailable. The ML toolchain DEEP (Date Efficient Evaluation Platform) from Fraunhofer IKS addresses this challenge.

From medical diagnosis to quality control, visual information is often used to make important decisions. Automating these decisions can relieve the burden on experts and avoid errors. This requires complex AI systems whose development involves large amounts of data, and which are generally unable to make reliable predictions. FAST attempts to provide an answer to these problems: a framework for developing reliable AI systems with little data.