Simulations of microstructure coupling with moving molten pool by selective laser melting using a cellular automaton

2021

Materials

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.

where

is the liquidus slope,

is the initial equilibrium concentration, and

is the solute concentration in the liquid phase. IN718 contains many elements, but for simplicity, it is considered here to consist of only Ni-19at.%Cr.…”

Section: Methodsmentioning

confidence: 99%

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

2021

Materials

“…In modeling the element segregation behavior during solidification, the governing equation for solute diffusion is given as 15 = ∇. ( )∇ + ( , * − , * ) ,…”

Section: Element Segregationmentioning

confidence: 99%

“…Cellular Automaton (CA) is recognized as one of the best simulation methods for examining ultra-fast solidification processes such as SLM. Ao et al 15 used CA to simulate the microstructure of AlSi10Mg during SLM based on an analysis of the thermodynamic-driven growth kinetics of the solid-liquid interface and melt pool composition. Trivedi 16 examined the grain growth rate using the Kurz-Giovanola-Trivedi model.…”

Section: Introductionmentioning

confidence: 99%

Investigation into microstructure and element segregation of Inconel 713LC during SLM processing using modified cellular automaton model

2021

Preprint

A numerical and experimental investigation is performed into the feasibility of fabricating IN713LC components using selective laser melting (SLM) through an appropriate control of the solidification speed, microstructure formation and element segregation. A modified Cellular Automaton (CA) model is developed to explore the nucleation, grain growth and element segregation behavior of IN713LC during ultra-fast solidification. It is found that the undesired phase formation which occurs during SLM processing of IN713LC is caused by the micro segregation of Nb, Ti, Mo and C at the grain boundaries. It is further shown that the micro segregation intensity depends on the solidification speed, which is determined in turn by the laser energy density. In particular, a lower laser energy density increases the solidification speed and results in a more uniform solid phase, thereby reducing the risk of crack formation. The simulation results are verified by experimental investigation. The results confirm that a lower laser energy density reduces the crack density and crack length. Finally, a crack-free IN713LC SLM sample is successfully produced by reducing the energy density from 360 to 170 J/m.

“…Cellular Automaton (CA) is recognized as one of the best simulation methods for examining ultrafast solidification processes such as LPBF [10][11][12][13]. Ao et al [14] used CA to simulate the microstructure of AlSi10Mg with a moving molten pool by selective laser melting. They have used the thermodynamic driven growth kinetics of the solid/liquid and composition.…”

Section: Introductionmentioning

confidence: 99%

Microstructure and Elements Concentration of Inconel 713LC during Laser Powder Bed Fusion through a Modified Cellular Automaton Model

2021

Crystals

In this study, a hybrid finite element (FE) and cellular automaton (CA) model is developed to explore crystallization behavior and alloying of Inconel713LC during Laser powder bed fusion. A cellular automaton model is considering the surface nucleation, equiaxed bulk nucleation, and grain growth kinetics. In addition, the equation for solute diffusion is coupled with a cellular automaton model to simulate the IN713LC elements segregation. During the phase change, the non-equilibrium segregation model is applied to insert the effect of ultra-fast solidification happening during LPBF. It is found that, during LPBF processing of IN713LC, the micro segregation of Nb, Ti, and C is accrued at the grain boundaries. It is further shown that the micro segregation intensity depends on the solidification speed, which is determined in turn by the laser heat input. In particular, a lower laser heat input increases the solidification speed and results in a more uniform solid phase, thereby reducing the risk of crack formation. Finally, using a comparison between simulation results and experimental observation, it was shown that the proposed model successfully predicts the bulk element concentration of IN713LC after laser melting.