Analytical modeling of grain size prediction in additive manufacturing

Additive manufacturing has been broadly employed to create geometrically complex parts, where processing parameters significantly influence the resulting parts’ materials microstructure and properties. However, additive manufacturing still faces limitations such as anisotropic microstructure and mechanical properties, restrictions on material selection, defects, and high cost. Grain size, usually quantitatively represented by mean grain diameter, is a vital microstructure feature closely linked to strength properties. An accurate physics-based analytical model is yet to be developed fully for process-structure–property prediction. In this work, the authors first develop the thermal model, considering heat transfer boundary conditions and molten pool geometry. Then, the grain size is simulated with both the heating and cooling processes considered, including thermal stress consideration, Johnson-Mehl-Avrami-Kolmogorov kinetics, and grain refinement. The microstructure-affected properties are also included to improve this analytical grain size model. In particular, the texture is first simulated using the columnar-to-equiaxed transition model, thermal dynamics, and Bunge calculation. After establishing the texture distribution, the visco-plastic self-consistency model acquires the properties of the affected materials. Then, the updated properties are embedded into the grain size model for greater accuracy. The Ti-6Al-4V alloy was chosen to showcase the effectiveness of analytical models in a multi-phase scenario. By using advanced models, prediction accuracy was significantly enhanced. Validation against experimental data demonstrated a mean agreement of 93.03% for grain size prediction across scanning speeds, reaching 97.01% at the industrially relevant 600 mm/s. Compared to prior analytical models, the developed framework achieved a mean accuracy improvement of 23.8%, with a peak improvement of 34.6% at 600 mm/s. The precision and dependability of the results obtained through this approach make it a valuable tool for future research and industrial development. This work provides a novel framework for improving the modeling of the microstructure features of materials more accurately while taking evolving property values into account, in addition to a sound analytical method for modeling grain size in additive manufacturing.