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The industrialisation of Additive Manufacturing (AM) requires a holistic data manThe industrialisation of Additive Manufacturing (AM) requires a holistic data management and integrated automation. INTEGRADDE aims to develop an end-to-end Digital Manufacturing solution, enabling a cybersecured bidirectional dataflow for a seamless integration across the entire AM chain.

The goal is to develop a new manufacturing methodology capable of ensuring the manufacturability, reliability and quality of a target metal component from initial product design via Direct Energy Deposition (DED) technologies, implementing a zero-defect manufacturing approach ensuring robustness, stability and repeatibility of the process. To achieve this aim, INTEGRADDE addresses following key innovations:

• Development of an intelligent data-driven AM pipeline.
• Combination of automatic topology optimisation algorithms for design, multi-scale process modelling, automated hardware-independent process planning, online control and distributed NDT for the manufacturing of certified metal parts.
• A self-adaptive control is adopted focused on the implementation of non-propagation of defects strategy. Moreover, Data Analytics will provide a continuous refinement by acquiring process knowledge to assist in the manufacturing of new metal components, improving right-first-time production by adopting a mass customization approach
• Cybersecurity ensures data integrity along the AM workflow, providing a novel manufacturing methodology for the certification of metal AM parts.

INTEGRADDE implements a twofold deployment approach for the pilot lines: both in application-driven at five industrial end- users (steel, tooling, aeronautics, and construction) and open-pilot networks at RTOs already owning AM infrastructure. This will allow a continuous validation and deployment of specific developments towards industrialization, boosting definitive uptake of AM in EU metalworking sector.

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Meet the Principal Investigator(s) for the project

Professor Zidong Wang - Zidong Wang is a member of Academia Europaea, a Member of the European Academy of Sciences and Arts, an IEEE Fellow and Professor of Computing at 成人直播app, UK. He has research interests in intelligent data analysis, statistical signal processing and dynamic systems & control. He has been named as the Hottest Scientific Researcher in 2012 in the area of Big Data and listed as highly cited researchers in categories of both computer science and engineering in 2015-2020 with an h-index of 139. He is currently serving as the Editor-in-Chief for International Journal of Systems Science, the Editor-in-Chief for Neurocomputing, the Editor-in-Chief for Systems Science and Control Engineering, and Associate Editor for other 12 prestigious journals including 5 IEEE Transactions.  His research has been funded by the EU, the Royal Society and the EPSRC. 

Related Research Group(s)

Digital Manufacturing - Being at the forefront of solutions for building smart machines, we create an operational framework for the digital transformation to Industry 4.0.

Interactive Multimedia Systems - Building sensor and media-rich, cross-layer, inclusive e-systems, with an interest in human-machine interaction, sensorial-based interfaces, data visualisation and multimedia.

Project last modified 20/07/2021