During combustion of steel mill gases acid dew point temperature varies strongly. SafeDewPoint aims to recover waste heat in hot […]
DeepQuality – Use of robust deep learning methods for the automatic quality assessment of steel products
Deep Learning aims to improve the automatic quality assessment of steel products by means of a holistic approach combining deep learning technology with sophisticated management of underlying training data to enable the optimal use of all available data sources and simultaneously simplify the configurability and maintainability of previous decision support system.
This project combines deep learning technology with sophisticated management of underlying training data and consists of the following concepts realizing a human-centered lifecycle for the robust industrial application of deep learning quality models.
- Production data pipelines
- Industrial Training Data Management
- Robust Application of Deep Learning techniques
- Online application of Deep Learning quality models