Pilot 6:
Root cause analysis for quality deviations and complex process chains.

Partner

Description

The high quality of products is very important for manufacturers of flat steel products in European and international competition. The work intensity of the employees responsible for the production process and product quality is correspondingly high. They must consider a very large amount of information (process data, quality data, the customer’s intended use, complaints, information from the starting material) to be able to identify and eliminate the causes of deviations quickly and in a targeted manner. Specialists from a range of disciplines work together to identify the causes of problems and resolve them as quickly as possible.

In this pilot application, an AI assistance system will be developed that automatically analyses the available data and identifies the causes of deviations from the normal state. For the development of the AI assistance, the knowledge of the domain experts has to be taken into account and an appropriate workflow has to be developed for reliability in case of cross-domain use of data & knowledge.

Method development:

  • Procedure model for interaction at human-AI-human interfaces in workflow (IAW)

People

Norbert Holzknecht
Norbert Holzknecht
BFI
Jens Brandenburger
Jens Brandenburger
BFI
Prof. Dr. Uta Wilkens
Prof. Dr. Uta Wilkens
Ruhr-Universität Bochum,
Institut für Arbeitswissenschaft
Prof. Dr. Christian Meske
Prof. Dr. Christian Meske
Ruhr-Universität Bochum,
Institut für Arbeitswissenschaft
Dr. Valentin Langholf
Dr. Valentin Langholf
Ruhr-Universität Bochum,
Institut für Arbeitswissenschaft
Prof. Dr. Laurenz Wiskott
Prof. Dr. Laurenz Wiskott
Ruhr-Universität Bochum,
Institut für Neuroinformatik
Enrico Bunde
Enrico Bunde
Ruhr-Universität Bochum,
Institut für Neuroinformatik
Prof. Dr. Tobias Glasmachers
Prof. Dr. Tobias Glasmachers
Ruhr-Universität Bochum,
Institut für Neuroinformatik
Max Bauroth
Max Bauroth
Ruhr-Universität Bochum,
Institut für Neuroinformatik