Research Progress on the Microstructure, Mechanical Properties, and Corrosion Behavior of TC4 Alloy Fabricated by Selective Laser Melting

Selective laser melting (SLM), a pivotal additive manufacturing (AM) technology for titanium alloys, enables near-net-shape forming of complex structures with relative densities of up to 99.9%, making it indispensable in aerospace, biomedical, and marine engineering. This review comprehensively updates the state of the art on SLM-fabricated TC4 (Ti-6Al-4V) alloy, addressing critical gaps in previous studies by integrating novel research progress, in-depth mechanistic analyses, and multi-dimensional comparisons. The core focus is on the unique thermal cycle (106–108 °C/s heating/cooling rates) of SLM, which induces a predominant needle-like martensitic α′ phase (99.7%) and minimal β phase (0.3%), leading to intrinsic anisotropy and low ductility. Room-temperature tensile strength reaches 1315.32 MPa with 9.6% elongation, and high-cycle fatigue limits the range from 417 to 829 MPa, strongly dependent on process parameters and post-treatment. Corrosion anisotropy is systematically analyzed: the XY plane (parallel to scanning direction) exhibits superior corrosion resistance in 1 M HCl (fewer pits and lower corrosion current density) and 3.5% NaCl (more stable passive film) compared to the XZ plane (deposition direction). Novel insights include: (1) synergistic effects of SLM process parameters (laser power–scanning speed–hatch spacing) on defect evolution and microstructure uniformity; (2) atomistic mechanisms of α′→α + β phase transformation during post-heat treatment; and (3) corrosion–mechanical coupling behavior in harsh environments (e.g., marine and biomedical). Post-treatment strategies are refined: annealing at 800 °C for 2 h achieves 1099 MPa tensile strength and 17.4% elongation, while hot isostatic pressing (HIP) reduces porosity from 0.08% to 0.01% and weakens fatigue anisotropy. This review also identifies unresolved challenges (e.g., in situ defect monitoring and multi-field regulated performance) and proposes future directions (e.g., AI-driven process optimization and functional gradient structures).