The already completed research project PRESED (Predictive Sensor Data mining for Product Quality Improvement) developed new methodologies and tools to help steel production plants to improve the quality of their products and to reduce their manufacturing costs by focusing primarily on three quality criteria: material surface appearance, internal material health, and mechanical material properties.
The goal of the project was to develop new methodologies and tools to help plants to improve the quality of their products and to reduce their manufacturing costs by focusing primarily on three quality criteria: material surface appearance, internal material health, and mechanical material properties.
These tools should allow to:
- optimize the manufacturing process by identifying the main causes of low quality and to
- predict the quality of the product as soon as possible to better characterize it and reduce the cost.
To achieve this goal and to make a major breakthrough in the application of data mining approach in the steel industry, we propose to contribute to new research areas recently developed in the field of data mining.
These new approaches are designed to extract knowledge from huge amount of complex data: sensorial time series of very large number of parameters (several hundred) registered for a substantial period of time (2-3 years) and a high frequency (1-10Hz). Indeed, only summary information (e.g.; casting speed average) was used for statistical analysis. To analyse automatically and massively these sensorial time series data, we propose a comprehensive solution built around five main axes.
European Data Forum 2016, Eindhoven (Poster)
1st International Data Science Conference 2017, Salzburg (Paper)
David Arnu, Edwin Yaqub, Claudio Mocci, Valentina Colla, Marcus Neuer, Gabriel Fricout, Xavier Renard, Christophe Mozzati and Patrick Gallinari: A Reference Architecture for Quality Improvement in Steel Production
Fraunhofer IFF-Wissenschaftstage 2017 (Talk in German)
July 1st, 2014 - December 31, 2017
Funding Grant Number
The project is co-financed by the Research Fund for Coal and Steel of the European Commission. It is part of the Technical Group Steel 9: Factory-wide control, social and environmental issues.