Industrial Digital Technologies Project

Application of Industrial Digital Technologies to the Metal Sector.

Industrial Digital Technologies Project


Funded by Innovation UK

Project Summary

As part of the UK government funded PRISM programme of research and innovation for the UK steel and metals sector, the Materials Processing Institute is conducting research into the application of industrial digital technologies at its Normanton steel plant.

Dates - April 2021 - Ongoing

Sponsor - Innovate UK


The fourth industrial revolution, or Industry 4.0, is revolutionising the way companies operate through the use of Industrial Digital Technologies (IDTs). These include: the Industrial Internet of Things (IIoT); robotics; automation; additive layer manufacturing; artificial intelligence and analytics; simulation; augmented and virtual reality, and cloud-based platforms. So-called Smart Factories are proliferating but these usually involve digitisation using new equipment. In the foundation industries, there is a huge amount of legacy equipment with minimal connectivity.

As part of the UK government funded PRISM programme of research and innovation for the UK Steel and Metals sector the Materials Processing Institute is conducting research into the Application of Industrial Digital Technologies at its Normanton Steel Plant. The Institute is investigating the use of Augmented Reality (AR), Machine Learning, Industrial Internet of Things, new sensor technology and blockchain technologies to optimise the process and realise energy and CO2 savings. The objective is to create a showcase for the application of these technologies for the Steel and Metals sector by creating a demonstrator in a working steel plant, showing how all the associated challenges of applying Industry 4.0 technologies to brownfield sites can be overcome.

For the first stage, the project was split into several areas:

  • Supply chain interaction looking at how the supply chain can be digitised to improve integration and transparency between the various stakeholders.
  • Development of an IIoT platform using ThingWorx. This involves connecting all existing data streams and additional sensors to the IIoT platform and developing data displays that can be accessed remotely.
  • Big data analytics to develop techniques for accessing and analysing the data which is available on the IIoT platform. This includes pre-processing of the data to remove outliers and data anomalies.
  • Machine learning and artificial intelligence (AI) for the casting process to realise the full potential of new sensor data and improved PLC communications. AI and machine learning models for predictive maintenance, providing operator advice and for quality prediction.
  • Development of a Digital Twin of the pilot caster to improve caster optimisation by allowing off-line testing of process changes.
  • Use of augmented reality (AR) to improve data presentation to operators and to deliver enhanced training and procedural information.