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MFILAMUXIAML SIGNED

Metal flow in laser additive manufacturing using x-ray imaging and machine learning

Total Cost €

EC-Contrib. €

Partnership

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MFILAMUXIAML project word cloud

Explore the words cloud of the MFILAMUXIAML project. It provides you a very rough idea of what is the project "MFILAMUXIAML" about.

point solid critical damaged machine dynamic velocities bead temperature melt surrounded quality routes proper tungsten injected metal filler track free base defect learning specimen repairing quick optimizing molten mixed observe achievement particles dynamics manufacturing powders camera tracers industry laser flow hot beam reveal parts nozzle size combined host special choosing first visualize powdery efficiency guide deposition onto surface profile imaging price printed difficult fundamental pool melting technique additive microstructure novelty am liquid flows ray keyhole lmd

Project "MFILAMUXIAML" data sheet

The following table provides information about the project.

Coordinator	UNIVERSITY COLLEGE LONDON Organization address address: GOWER STREET city: LONDON postcode: WC1E 6BT website: n.a. contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.
Coordinator Country	United Kingdom [UK]
Total cost	212˙933 €
EC max contribution	212˙933 € (100%)
Programme	1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
Code Call	H2020-MSCA-IF-2019
Funding Scheme	MSCA-IF-EF-ST
Starting year	2020
Duration (year-month-day)	from 2020-08-01 to 2022-07-31

Partnership

Take a look of project's partnership.

#	participants	country	role	EC contrib. [€]
1	UNIVERSITY COLLEGE LONDON UNIVERSITY COLLEGE LONDON Organization address address: GOWER STREET city: LONDON postcode: WC1E 6BT website: n.a. contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	UK (LONDON)	coordinator	212˙933.00

Map

Project objective

Additive Manufacturing (AM) has been a hot topic for many years. A fundamental understanding of the metal flows in the molten pool is critical to improving the quality of the sample produced by AM. Laser metal deposition (LMD) is one of the most widely used AM methods, which has a high production efficiency, and a special application in repairing the damaged parts with large size and high price. During the LMD process, a powdery filler is injected from the nozzle onto the surface of the base metal, and a laser beam is used to melt the powders and surface of a specimen. Dynamics of the keyhole and molten pool will determine the temperature distribution and the profile of the molten pool, thus affecting the microstructure of the printed bead. It is difficult to observe the dynamics of the molten pool in AM directly with a camera because the molten pool is surrounded by the solid metal. The objectives of this proposal are to reveal the dynamic characteristics of the keyhole and molten pool in LMD and provide a guide for choosing the proper production parameters for defect-free AM. To achieve the objectives, the X-ray imaging system in the host institute will be used to observe the dynamics of the keyhole and molten pool. With the high-melting-point tungsten particles as the tracers mixed with the metal powders, the flow of the liquid metal in the molten pool could be observed. The machine learning technique is then used to track the flow of the tungsten particles, which makes the quick determination of the flow routes and velocities of the liquid metal possible. The novelty of this proposal is that the x-ray imaging method combined with the machine learning technique is first used to visualize the dynamics of the keyhole and molten pool in LMD. This research could provide a guide for optimizing the process parameters and improving the AM quality, and the achievement of this research could contribute to the development of LMD in the industry.

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The information about "MFILAMUXIAML" are provided by the European Opendata Portal: CORDIS opendata.