In new-generation manufacturing, intelligence, networking, and digitalization are prioritized in the worldwide agreement on decarbonization, green, and sustainable production. Particularly in analytics digitalized methodology, additive manufacturingAdditive manufacturing (AM) enables intricate fabrication, decreased material waste, flexible design, and economic impact. With limitations such as anisotropic microstructureMicrostructure and properties, restrictions on material selection, defects, and high-cost metal AM still to be overcome, this research focuses on investigating the microstructure evolutionMicrostructure evolution, emphasizing texture and grain size based on processing parameters and affected multi-phase materials performance, such as elastic modulus, Poisson’s ratio and yield strength. The authors developed the thermal model, considering heat transfer boundary and molten pool geometry. Then, the grain size is simulated with both the heating and cooling processes considered, including thermal stressThermal stress, JMAK (Johnson-Mehl-AvramiKolmogorov), and grain refinementGrain refinement. The texture is simulated via the CET (Columnar-to-equiaxed transition of crystallographic orientation) model, thermal dynamics, and Bunge calculation. The self-consistency model acquires the properties with established texture distribution. Then, the microstructureMicrostructure-affected and non-affected residual stressResidual stress are modeled and compared.