Prediction of Epitaxial Grain Growth in Single-Track Laser Melting of IN718 Using Integrated Finite Element and Cellular Automaton Approach

Abstract

The mechanical properties of selective laser melting (SLM) components are fundamentally dependent on their microstructure. Accordingly, the present study proposes an integrated simulation framework consisting of a three-dimensional (3D) finite element model and a cellular automaton model for predicting the epitaxial grain growth mode in the single-track SLM processing of IN718. The laser beam scattering effect, melt surface evolution, powder volume shrinkage, bulk heterogeneous nucleation, epitaxial growth, and initial microstructure of the substrate are considered. The simulation results show that during single-track SLM processing, coarse epitaxial grains are formed at the melt–substrate interface, while fine grains grow at the melt–powder interface with a density determined by the intensity of the heat input. During the solidification stage, the epitaxial grains and bulk nucleated grains grow toward the top surface of the melt pool along the temperature gradient vectors. The rate of the epitaxial grain growth varies as a function of the orientation and size of the partially melted grains at the melt–substrate boundary, the melt pool size, and the temperature gradient. This is observed that by increasing heat input from 250 J/m to 500 J/m, the average grain size increases by ~20%. In addition, the average grain size reduces by 17% when the initial substrate grain size decreases by 50%. In general, the results show that the microstructure of the processed IN718 alloy can be controlled by adjusting the heat input, preheating conditions, and initial substrate grain size.

Development of Optimal L-PBF Process Parameters using an Accelerated Discrete Element Simulation Framework

“…Current parameter optimization studies utilizing numerical simulations often model a single melt track [16][17][18][19][20]. From the results, meltpool dimensions and porosity formation in this melt track are analyzed.…”

Grid anisotropy reduction method for cellular automata based solidification models

“…This allows the efficient distinction and transition between different states (solid, liquid and their interface) of matter. Therefore, CA methods have been applied in various growth models for predictions which include: tumour growth [7,8,9], epitaxial growth [10,11,12], urban growth [13,14] and solidification in binary alloys [15,16,17,18]. These models, based on the physical laws, perform growth within the interface cells These capture rules demonstrate a major limitation of the CA approach, namely artificial (grid) anisotropy caused by the CA square grid.…”

Modeling the Evolution of Grain Texture during Solidification of Laser-Based Powder Bed Fusion Manufactured Alloy 625 Using a Cellular Automata Finite Element Model

“…CA models have gained popularity for simulating the grain texture evolution in alloys manufactured by PBF-LB due to the advancements in the field of additive manufacturing. Typically, 2D CA models are used for their computational efficiency while being capable of simulating realistic grain textures [19,20]. Dezfoli et al [19] used a 2D CA model to simulate the grain texture from single-track PBF-LB processed alloy 718.…”