Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/180895
Title: | Improving forming quality and corrosion resistance of CMT-P additive manufactured 2024 aluminum alloy using assisted laser | Authors: | Zhang, Zhiqiang Li, Hanxi He, Shiwei Zhou, Wei Qi, Yang |
Keywords: | Engineering | Issue Date: | 2024 | Source: | Zhang, Z., Li, H., He, S., Zhou, W. & Qi, Y. (2024). Improving forming quality and corrosion resistance of CMT-P additive manufactured 2024 aluminum alloy using assisted laser. Journal of Manufacturing Processes, 124, 1025-1036. https://dx.doi.org/10.1016/j.jmapro.2024.06.071 | Journal: | Journal of Manufacturing Processes | Abstract: | This study aims to improve the forming quality and corrosion resistance of 2024 aluminum alloy fabricated through CMT-P additive manufacturing using assisted laser. The effects of varying laser powers on forming characteristics, microstructural evolution, and corrosion behavior were investigated. Results reveal that increasing laser power influenced macroscopic morphology and porosity, with higher powers leading to improved forming accuracy and reduced porosity until a threshold is reached. Microstructural analysis demonstrates that 2500 W laser assistance, where a transition of molten pool mode to keyhole mode occurred, refines grain morphology, reduces precipitate content. Corrosion resistance assessments indicate that sample fabricated with assisted laser power of 2500 W exhibited the highest resistance due to reduced porosity, finer grain size, and fewer precipitates. This study contributes valuable insights into optimizing the additive manufacturing process for 2024 aluminum alloy, enhancing mechanical integrity and longevity in demanding applications. | URI: | https://hdl.handle.net/10356/180895 | ISSN: | 1526-6125 | DOI: | 10.1016/j.jmapro.2024.06.071 | Schools: | School of Mechanical and Aerospace Engineering | Rights: | © 2024 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. | Fulltext Permission: | none | Fulltext Availability: | No Fulltext |
Appears in Collections: | MAE Journal Articles |
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