Current & past projects

SeSy

Development of self-learning systems in an industrial environment

Machine learning systems (ML systems) used in industrial applications for quality control are usually customized for the respective application. This implies that the training data must be collected specifically for the respective problem and, in particular, carefully labeled. This process is both time-consuming and costly. The aim is to create a system that contains algorithms and models to simplify this process. The aim is to ensure that recurring, similar and at the same time complex analysis tasks can be handled automatically and with as little effort as possible. The detection of anomalies, outliers or deviations helps to improve the process by making it much easier to label the learning data.

One Pager SeSy1.00 MBPNG

FiAZ

Transferable framework for interpretable anomaly detection for industrial time series data

In industrial applications, the selection of an ML model is not only based on quality metrics, but also on the interpretability, traceability and thus implicitly also trustworthiness of the models. The aim is to develop AI processes, procedures for anomaly detection in industrial time series that are more powerful and yet interpretable.

One Pager FiAZ346 KBPNG

KIUPRO

AI-supported process stabilization for 3D printing and polishing

The aim of the KIUPRO project is to use AI methods, especially machine learning, to stabilize both 3D plastic printing and the computer-controlled polishing process on the basis of process sensor technology and thus achieve higher component quality. As a result, a machine learning model is to be made available that is able to predict quality parameters for different processes based on sensor data.

One Pager KIUPRO551 KBPNG

KI-REN Marketplace

AI-supported marketplace for regionally generated renewable electricity based on dynamic price models

The aim of this research project is to develop and implement an integrated platform that uses AI to forecast consumption and generation data to best match regional/local demand and regional/local supply of electricity from renewable energies on a marketplace using dynamic pricing models.

One Pager KI-REN Marketplace569 KBPNG

KI ProWitA

AI-supported process optimization for the recycling of post-industrial waste

The aim of the scheme, enterprise, goal is to use artificial intelligence (AI) to enable the recycling of post-industrial waste for new components to the extent that the quality requirements for a new product are met.

One Pager AI ProWitA431 KBPNG

TRANGAN

Improvement of detection accuracy by extension of condition classes for transient airborne and structure-borne noise signals using artificially generated process data.

The aim of the TRANGAN project is to develop a generally applicable method for artificial data generation. This method should enable the creation of artificial transient process data. The impact of the solution approach is the further development of known methods for GAN data generation from quasi-stationary signals to the generation of transient signals. This significantly simplifies the development of AI-based condition monitoring systems with the requirement for high classification accuracy and shortens the development time by reducing the number of tests required.

One Pager TRANGAN412 KBPNG

HCKIS

Human-centered AI system

A human-centered AI system [HCKIS] is to be set up, which will enable smart networked machines to interact with humans on different levels using AI systems. As part of, within the framework of the goal, a joint technology carrier is to be set up, which will be used in particular to demonstrate methods for real-time-based process analysis and quality assurance using artificial intelligence.

One Pager HCKIS389 KBPNG