journal article·2025-12Open access

Effect of heat treatment on microstructure and mechanical properties of 316L/IN718 graded materials prepared by high-throughput SLM

Meixia Su, Weihan Chu, Wei Chen, Yu Liu, Fengchun Jiang, Changjun Wang

Materials & Design· Volume 260

Abstract

• Defect-free 316L/IN718 FGMs were produced zone-by-zone via high-throughput SLM parameter tuning. • Multi-zone element mapping reveals quantitative links among Cr/Ni gradient, cooling rate, recrystallisation and phase evolution, guiding cross-scale heat-treatment design. • Solution temperature and ageing regime effects on microstructure and properties are clarified, providing rules for strength-ductility synergy in FGMs. Functionally graded materials (FGMs) can alleviate compositional mismatch and property discontinuities in multi-material components, yet the post-processing window that optimizes microstructure–property balance remains unclear. We fabricate 316L/IN718 FGMs by high-throughput selective laser melting (SLM) with zone-wise parameter tuning, and systematically assess how representative heat-treatment routes affect phase evolution and tensile response. EBSD/SEM-EDS/TEM/XRD reveal that as-deposited FGMs contained mixed columnar/equiaxed grains. Post-treatment diffusion eliminated melt-pool traces, fragmented chain-like Laves into grain-boundary particles and precipitated abundant γ″ plus minor γ′ within grains. Solution treatment at 980 °C introduced acicular/rod-like δ-Ni 3 Nb at grain boundaries, impairing ductility. After heat treatment, tensile strength increased, yield strength decreased, and elongation peaked at 25.1 %. These findings delineate the competing roles of γ″ strengthening, δ-Ni 3 Nb formation, and microstructural relaxation, providing practical guidance for tailoring post-processing to optimize graded Ni-steel alloys.
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Research context

OpenAlex topics: Additive Manufacturing Materials and Processes · High Entropy Alloys Studies · Solidification and crystal growth phenomena

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

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