MFILAMUXIAML · Metal flow in laser additive manufacturing using x-ray imaging and machine learning
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2021-02-01 → 2023-09-30
- EU contribution
- €212,934
- Participants
- 1
- Scheme
- MSCA-IF
Lines connect the coordinator with its partners.
Results in brief
Metal flow in laser additive manufacturing using x-ray imaging and machine learning
Problem/issue being addressed: Laser powder bed fusion (LPBF) technology fabricates parts layer by layer via a focused laser beam to fuse the loose powder along the pre-designed path, is promising in aerospace, automotive and medical for its customization and free of geometric limitations that can’t be realized by the traditional technologies. A keyhole pore forms when gas is trapped by the cavity. In LPBF, when the melt pool transforms form conduction mode to keyhole mode, which is beneficial for low laser-absorptivity material, it is likely to generate a keyhole pore. Therefore, conducting in-depth research on the formation mechanism of keyhole pore is needed. Importance: AM is a collection of emerging manufacturing techniques, e.g., Laser powder bed (LPB), laser metal deposition (LMD), electron beam powder bed (EBPD), and has the potential to make greater breakthrough than conventional methods. AM offer many advantages, e.g. of making complex components with relatively low cost. A fundamental understanding of the laser-metal powder interaction is critical to improving the quality and efficiency in the laser AM process. Our research provides a strategy to reduce or remove the pore in laser powder bed fusion, which is extremely important for the mass production of additive manufacturing technology. Overall objectives: a three-dimensional thermal-mechanical-fluid coupled model is established via finite volume method, considering the heat transfer, fluid flow, recoil pressure as well as solidification drag model, and reproduces the formation processes of three representative pores. The proposed model is validated by the in-situ X-ray imaging results and reveals the fluid flow, keyhole fluctuation, three types of pores formation process and mechanism under a range of high-speed welding and powder bed fusion conditions. The goal of this research is to provide a comprehensive understanding of keyhole induced-porosities in laser powder bed fusion of aluminium and suggests the strategy towards pore-free laser fusion. Conclusions: Through the research of the action, we clearly clarify the mechanism for pore formation in laser additive manufacturing and suggests the method to avoid the pore. Our work is beneficial to improving the quality of AM and promote its application in many fields, e.g., aerospace, transportation, automobile, and so on.
Data: CORDIS, © European Union
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.
Original text from CORDIS.
Participants
- UNIVERSITY COLLEGE LONDON · LondonCoordinatorUnited Kingdom
Links
Data: CORDIS, © European Union
