Quality Prediction

IMPROVE provides an innovative holistic decision support system (DSS) and a prediction tool for quality monitoring to assist the machine operator. The decision support app includes a visualisation of cause and effect relations to help the operator in maintaining high quality.

Central characteristics of our user support tools:

  • Supporting operators with results from complex data analysis models
  • Predicting the quality by using data-driven models based on machine parameters
  • Developing of cause and effect graphs for a selected quality parameter, representing the complex causal dependencies of a total plant in regard to the quality parameter
  • Preventing scrap and off-spec products due to in-line quality prediction in comparison to expensive off-line lab measurements

For more information, please contact our coordinator Prof. Dr Oliver Niggemann.

You wish to receive information for special stakeholders? Please find the specific information linked below.

Prof. Dr. Birgit Vogel-Heuser (TUM) gives insights into the IMPROVE solution