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Research Article

The impact of coaxial gas technology on the morphology of powder by gas atomisation and the additive manufactured mechanical performance

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Article: e2375107 | Received 09 Apr 2024, Accepted 25 Jun 2024, Published online: 22 Jul 2024
 

ABSTRACT

Metal powders are essential for additive manufacturing (AM), with their morphology significantly impacting the performance of AM components. Gas atomisation (GA) is a common method for producing metal powders due to its efficiency and cost-effectiveness. However, GAed powders often exhibit defects such as alien powders, satellite powders, and hollow powders, which can adversely affect the AM process. To address these challenges, coaxial gas technology has been introduced and analysed through numerical simulation. Our study involved comparing GA processes with and without coaxial gas technology to generate two types of powders. These powders were assessed using Synchrotron X-ray computed tomography (SXCT) and classification algorithms. The pores in AM components manufactured from these powders were examined using SXCT, and their mechanical properties were evaluated through tensile tests. The findings of our research validate the effectiveness of coaxial gas technology in reducing powder defects and enhancing the performance of AM components.

Acknowledgments

We thank the support of the Shanghai Synchrotron Radiation Facility for providing the beam time for the synchrotron X-ray CT work conducted on the BL16U2 and BL13HB beamlines.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and code availability

Data availability The SXCT data are available under restricted access for having the large size in the order of TBs, which cannot be stored or transferred on commonly available data-sharing platforms, access can be obtained by request from the corresponding author. Analysis of results was performed by MATLAB with in-house developed codes, which can be accessed at links: https://github.com/Roshan-sjtu/powder-morphology-data.

Additional information

Funding

This work is sponsored by the National Key Research and Development Program of China (grant number 2023YFB3712003), National Natural Science Foundation of China (52105469 and 52075327), SJTU Global Strategic Partnership Fund (2023 SJTU-CORNELL), the innovation foundation of Commercial Aircraft Manufacturing Engineering Center of China (No. 3-0410300-031), and the University Synergy Innovation Program of Anhui Province (GXXT-2022-086)