REVIEW article
Front. Nucl. Eng.
Sec. Nuclear Materials
Volume 4 - 2025 | doi: 10.3389/fnuen.2025.1603437
This article is part of the Research TopicApplications of Additive Manufacturing to Nuclear Material EngineeringView all articles
Role of Additive Manufacturing in Developing Functionally Graded Materials for Nuclear Applications
Provisionally accepted
- Amrita School of Engineering, Chennai, Chennai, India
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The global acceptance of additive manufacturing has evolved with time and has proven to provide promising solutions to varied critical requirements of the nuclear industry. The components of a nuclear reactor, when built using additive manufacturing techniques, offer high microstructural control, making them versatile for a range of properties. These properties can be made easily achievable and tailorable by using functionally graded materials. The nuclear components with a wide range of properties are essential, as the environment inside and outside the reactor varies drastically. This study reviews the current progress in additive manufacturing techniques used for manufacturing functionally graded materials for nuclear applications, highlighting the gradient design methodologies and processing techniques. Additive Manufacturing techniques such as selective laser melting uses multiple powder feeders, and mechanical pre-mixing of powders along with controlled process parameters for effectively fabricating functionally graded materials. These materials possess superior mechanical properties (such as microhardness ranging up to 890 H00.5 and compressive strength up to 2040 MPa for FeCrCoNiMo0.5W0.75), thermal conductivity and thermal properties compared to monolithic counterparts. A comparative analysis of the manufacturing capabilities of the additive manufacturing techniques, along with the usage of advanced computational techniques such as AI in optimising process parameters for desirable strength and low defect generation, is also presented. The study emphasises on the need for strategies such as process parameters optimisation and data-driven design to fully utilise the potential of additively manufactured functionally graded materials in the nuclear sector.
Keywords: Additive manufacturing, Functionally graded material, Nuclear component, Gradient microstructure, machine learning
Received: 31 Mar 2025; Accepted: 28 Jul 2025.
Copyright: © 2025 Sasi, Srivastava and Dash. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Khushbu Dash, Amrita School of Engineering, Chennai, Chennai, India
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