Laser Additively Manufactured High-Entropy Alloys via Laser Powder Bed Fusion and Laser-Directed Energy Deposition: Process-Structure-Property Relationships and Design Strategies.

High-entropy alloys (HEAs) offer attractive combinations of mechanical performance, thermal stability, and compositional flexibility, making them promising candidates for advanced structural applications. Laser-based additive manufacturing, particularly laser powder bed fusion (LPBF) and laser-directed energy deposition (LDED), enables the fabrication of geometrically complex HEA components with non-equilibrium microstructures. However, the distinct thermal histories of LPBF and LDED, with typical cooling rates of approximately 105-107 K s-1 and 102-104 K s-1, respectively, strongly govern solidification behavior, elemental segregation, residual stress development, defect formation, and mechanical properties. Although previous reviews have discussed additively manufactured HEAs, an integrated framework linking composition design, printability, LPBF/LDED processing, microstructural evolution, post-processing, and industrial qualification remains limited. Therefore, this review establishes a unified composition-process-structure-property framework for laser additively manufactured HEAs. Fundamental HEA concepts, LPBF/LDED process characteristics, solidification behavior, phase formation, defect evolution, and mechanical performance from ambient to elevated temperatures are systematically discussed across representative FCC, refractory, and dual-phase HEA systems. This review emphasizes that printability should be considered during alloy design by correlating composition-dependent solidification characteristics, cracking susceptibility, phase stability, and defect formation with mechanical performance. Post-processing treatments are shown to modify residual stress, microsegregation, precipitation behavior, porosity, and deformation mechanisms, although their benefits must be balanced against thermal softening or brittle phase formation. Finally, CALPHAD, integrated computational materials engineering (ICME), machine learning (ML), and in situ monitoring are identified as promising tools for accelerating alloy and process optimization, while reproducible process windows, defect-control criteria, databases, and qualification protocols remain essential for industrial implementation.