DeepQuality

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

Recommendation

Rensodyn – Knowledge-based process management for better energy utilization

Initial situation: Alternating feedstocks at sintering plant and blast furnace Adaptations of the process management to changing boundary conditions necessary […]

DroMoSPlan – Drones for autonomous Monitoring of Steel Plants

Baseline situation – Drone technology is increasingly being used for civilian purposes and further developed in these areas. – For […]

TempKorroSchu

Situation: During re-heating of material for the production of special long products and forged screws 1 to 2 % of […]

Longlife Tuyere – New improved blast furnace tuyeres

Initial situation: Burning of tuyeres is the most common cause of unplanned shutdowns of blast furnaces A total non-productive energy […]