Researchers at the University of Sheffield in the United Kingdom have joined a project called INTEGRADDE, which aims to develop an end-to-end solution for the Directed Energy Deposition (DED) process for the metalworking industry. The project is reportedly supported by a European consortium of 17 million euros.
The INTEGRADDE project is led by Spain's AIMENCentro Tecnológico and involves 26 partners from 11 countries.
Teams from the three departments of the University of Sheffield are now supporting the project, including George Panoutsos, professor of computational intelligence, Matthew Gilbert, professor of civil engineering, and Iain Todd, professor of metallurgy.
Professor Todd said: "This project brings the possibilities of digital production lines closer together. The interdisciplinary nature of this work and the partnership established throughout Europe means that for the first time the entire AM supply chain is fully considered and we will see unprecedented Rapid development."
As part of the EU's "Horizon 2020" research and innovation program, the INTEGRADDE project aims to accelerate the adoption of industrial additive manufacturing. In the process, partners intend to reduce the manufacturing costs and unpredictable defects of metal 3D printed parts.
Therefore, research on building strategy optimization, multi-scale and multi-physics modeling, hardware independent construction process, online control and online quality assurance is being explored. This is expected to enable a strategy for overall optimization and control of the additive manufacturing process.
Professor Gilbert added: "Traditional manufacturing processes often impose severe constraints on the geometry of the parts that can be produced. Additive manufacturing releases many constraints and through DED processes, the size of parts produced by additive manufacturing can be significantly increased.
Through trial lines with partners ArcelorMittal, GKN Aerospace, CORDA, Loiretech, and MX3D, a data-driven computing framework will be used to extract process-component relationships from artificial intelligence models. In addition, powder laser metal deposition (LMD-p) will be used for these test lines.
This research is expected to increase the reliability of the additive manufacturing process by 40%, increase production speed by 25%, and improve the quality of the production steps. Researchers at the University of Sheffield will consider the material requirements for the trial line and how to incorporate these materials into the digital platform.