In this review article, the latest applications of machine learning (ML) in additive manufacturing (AM) field are reviewed. These applications, such as parameter optimization and anomaly detection, are classified into different types of ML tasks, including regression, classification, and clustering. The performance of various ML algorithms in these types of AM tasks are compared and evaluated. Finally, several future research directions are suggested.

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Design of multi-phase high entropy alloys uses metastability of phases to tune the strain accommodation by favoring transformation and/or twinning during deformation. Inspired by this, here we present Si containing dual phase Fe42Mn28Co10Cr15Si5 high entropy alloy (DP-5Si-HEA) exhibiting very high strength (1.15 GPa) and work hardening (WH) ability. The addition of Si in DP-5Si-HEA decreased the stability of f.c.c. (γ) matrix thereby promoting pronounced transformation induced plastic deformation in both as-cast and grain refined DP-5Si-HEAs. Higher yet sustained WH ability in fine grained DP-5Si-HEA is associated with the uniform strain partitioning among the metastable γ phase and resultant h.c.p. (ε) phase thereby resulting in total elongation of 12%. Hence, design of dual phase HEAs for improved strength and work hardenability can be attained by tuning the metastability of γ matrix through proper choice of alloy chemistry from the abundant compositional space of HEAs.