European consortium aims for additive advances

Sheffield University is part of a €17m European consortium developing the next generation of 3D printing technology capabilities to advance the reliability, speed and quality of additive processes.

additive
(Image Sheffield University)

The ‘Intelligent data-driven pipeline for the manufacturing of certified metal parts through Direct Energy Deposition’ (INTEGRADDE) project is led by AIMEN Centro Tecnológico in Spain, and involves 26 partners from 11 countries. The project aims to deliver a key objective of the European Commission, namely to advance Industry 4.0 by deploying additive technologies under real manufacturing conditions.

Manufacturing costs and the appearance of currently unpredictable defects in metal products and parts produced by additive manufacturing are limiting its widespread adoption. To address this, the consortium will develop a strategy of continuous and integral optimisation and control of the additive manufacturing processes, from product design to final verification. The project will also look to optimise current industrial pilot lines and processes using data science and artificial intelligence.

The project team estimate that it will increase the reliability of AM processes by 40 per cent, the production speed by 25 per cent, and provide a step-change in improving the quality of parts produced.

According to Sheffield University, INTEGRADDE will have an impact in the aeronautical, mechanical, automotive and civil construction sectors and will involve working with end-users including GKN Aerospace, ArcelorMittal, MX3D, Loiretech and CORDA.

Sensor network to turn additive bridge into ‘living laboratory’

Three engineering departments at the Sheffield University will support the consortium with underpinning research in data science, signal processing and artificial intelligence (Automatic Control and Systems Engineering – ACSE), research in structural and topological optimisation (Civil and Structural Engineering) as well as research in metallurgy and advanced material characterisation (Materials Science and Engineering).

Professor of Computational Intelligence, George Panoutsos, said: “The scope of the work is two-fold; to use raw process data to develop mathematical models of process-part behaviours, and to use such models to further understand and optimise the manufacturing process itself.”

The Department of Civil and Structural Engineering is expected to create novel optimisation tools that can be used to design large metal parts produced by additive manufacturing, tailored for use with Direct Energy Deposition (DED) additive manufacturing processes. They will work with end-user partners such as MX3D, who recently used DED AM to build a pedestrian bridge in Amsterdam.

Professor of Civil Engineering, Matthew Gilbert, said: “Conventional manufacturing processes usually impose severe constraints on the geometry of the components that can be produced. Additive manufacturing frees up many of these constraints, potentially allowing the strong and light component forms identified via optimization to be manufactured for the first time. And with DED processes, the scale of components that can be produced via additive manufacturing is significantly increased.”

The Department of Materials Science and Engineering will consider the materials requirements for AM processes, and how these can be incorporated into digital platforms.

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