AccScience Publishing / ESAM / Volume 1 / Issue 1 / DOI: 10.36922/ESAM025060005
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REVIEW ARTICLE

Additive manufacturing techniques for EH36 steels: Challenges and future directions

Lin Jie Justin Ang1 ,  Jiazhao Huang1 ,  Mui Ling Sharon Nai1 ,  Pan Wang1*
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1 Singapore Institute of Manufacturing Technology, Agency for Science, Technology and Research (A*STAR), 5 Cleantech Loop, Singapore
ESAM 2025, 1(1), 025060005 https://doi.org/10.36922/ESAM025060005
Received: 5 February 2025 | Revised: 16 March 2025 | Accepted: 17 March 2025 | Published online: 25 March 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Additive manufacturing (AM) has revolutionized the fabrication of metallic components, offering significant advantages in design flexibility, material efficiency, and process customization. EH36 steel, a high-strength, low-alloy material, is widely used in the marine and offshore industries due to its excellent mechanical properties and corrosion resistance. While AM presents a promising avenue for advancing the application of EH36 steel, several research gaps persist. This review provides a comprehensive overview of AM techniques applicable to EH36 steel, including powder bed fusion using a laser beam, direct energy deposition using a laser beam, and direct energy deposition using an electric arc. Key challenges in integrating additive manufactured (AMed) components with traditionally manufactured parts, such as optimizing interfaces in hybrid components and applying AM for in situ repair of large-scale marine structures, are examined, emphasizing their potential to significantly reduce costs and downtime. The review further addresses the critical need for standardization and certification of AMed EH36 steel components while proposing future research directions focused on advanced numerical simulations, digital twin technologies, and machine learning-driven process optimization to enhance their performance and reliability.

Keywords
Additive manufacturing
PBF-LB
DED-LB
DED-Arc
EH36
Marine and offshore
Funding
This research was supported by the Manufacturing, Trade, and Connectivity Programmatic Grant “Advanced Models for Additive Manufacturing (AM2)” (grant no.: M22L2b0111).
Conflict of interest
The authors have no conflicts of interest to disclose.
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Engineering Science in Additive Manufacturing, Published by AccScience Publishing
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