Use of robust deep learning methods for the automatic quality assessment of steel products

The critical significance of decision-making processes within manufacturing, particularly in intricate operations like steel production is a fundamental issue in production systems. These processes serve as the backbone of production systems, influencing every aspect of the manufacturing journey. It points out the hurdles that modern decision support systems (DSS) encounter, driven by escalating customer demands and the necessity for adaptable product features.

In response to these challenges, the DeepQuality project was initiated. Its primary objective is to enhance the automated quality assessment within steel production. To achieve this goal, the project employs a synergistic approach, merging cutting-edge deep learning technologies with sophisticated data management techniques. By doing so, the project aims to improve decision-making processes within the steel industry, ultimately optimizing production efficiency and product quality.

We would like to thank the Research Fund for Coal and Steel of the European Union for the funding of this project, Grant Agreement No. 101034037.