Analytical modeling of grain size prediction in additive manufacturing

Additive Manufacturing (AM) has been broadly employed to create geometrically complex parts, where processing parameters significantly influence the resulting parts’ materials microstructure and properties. However, AM 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, JMAK, 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 CET model, thermal dynamics, and Bunge calculation. After establishing the texture distribution, the visco-plastic selfconsistency 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, greater prediction accuracy was attained by comparing the prediction findings to experimental data from the literature and earlier analytical modeling results. 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 AM.