journal article·2026-02Open access

Tailoring microstructure and residual stress of tungsten/reduced activation steel dissimilar metals with CoCrNi interlayer fabricated by laser-directed energy deposition

Wei Feng, Wei Guan, Jiahui Chen, Dongze Chen, Lu Zhang, Rongwei Zhang, Zhonghan Liu, Yunhe Yu, Jixin Hou, Zhixin Xia

Abstract

Additive manufacturing of tungsten/steel (W/steel) dissimilar metal components with complex geometries have significant application demands in the nuclear industry. However, the formation of brittle intermetallic compounds (IMCs) and high residual stress at the interface restrict their mechanical performance. This study proposed a novel multi-component solid solution design strategy, incorporating a CoCrNi medium-entropy alloy interlayer for joining W/reduced activation ferritic/martensitic (RAFM) steel dissimilar metals via laser-directed energy deposition (LDED). Microstructural characterization revealed the formation of a stable FeCoCrNi solid solution at the steel/CoCrNi interface due to the high-entropy effect, resulting in excellent metallurgical bonding. Additionally, a layer of R-phase IMCs with a thickness of approximately 120 nm was observed at the W/CoCrNi interface, leading to increased lattice distortion. Finite element simulations were conducted to investigate the residual stress at the W/steel heterogeneous interface during LDED. The results demonstrated that involving CoCrNi interlayer effectively reduced residual stress from 1032 to 569 MPa. The tailored microstructure and residual stress of the W/steel dissimilar metals enabled a bonding strength of 156 MPa, meeting the minimum service requirements for the nuclear industry. This study provides a novel and effective approach for manufacturing high-performance W/steel dissimilar metal components.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Additive Manufacturing Materials and Processes · High Entropy Alloys Studies · Fusion materials and technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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