Understanding and control of gas porosity in metal laser powder-bed fusion additive manufacturing

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Understanding and control of gas porosity in metal laser powder-bed fusion additive manufacturing
Title:
Understanding and control of gas porosity in metal laser powder-bed fusion additive manufacturing
Journal Title:
International Journal of Computer Integrated Manufacturing
Publication Date:
18 January 2025
Citation:
Laskowski, R., Mikula, J., & Vastola, G. (2025). Understanding and control of gas porosity in metal laser powder-bed fusion additive manufacturing. International Journal of Computer Integrated Manufacturing, 1–16. https://doi.org/10.1080/0951192x.2025.2452984
Abstract:
The presented study discusses mechanism leading to gass porosity formation during additive manufacturing (AM) process. The method employs numerical scheme, which includes fluid dynamics of the melt pool and on-the-fly ray-tracing implemented within a phase field framework where the liquid/vapor interface is captured within volume of fluid approach. The emission of pores in the middle or the stripe happens only for relatively low scan speed (less than 0.6 m/s for IN718). The stripe edges (laser turning points) are identified as potential source of gas pores at scan speeds as high as 1 m/s. Simulations of laser turning, representative of the edge of a stripe, suggest the way the process parameters can be altered to avoid local porosity formation, while still ensuring a deep melt pool suitable for high build rates. The laser power modulation around the edge can lead to over 50% reduction of the pores emission. The proposed numerical framework requires only moderate computational resources, thus providing a holistic tool to decrease defect density and thus further improve quality of additive manufactured components.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Metal AM Powders: Reusability, Rejuvenation, Cost, Quality & Performance (RRAMP)
Grant Reference no. : M22K7a0047

This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Advanced Models for Additive Manufacturing (AM2)
Grant Reference no. : M22L2b0111
Description:
This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Computer Integrated Manufacturing on 18 Jan 2025, available online: http://www.tandfonline.com/10.1080/0951192X.2025.2452984.
ISSN:
0951-192X
1362-3052
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