Atomistic simulation of primary microstructure formation in metals during crystallization from the melt

Area of Science:

• Materials Science
• Computational Materials Science
• Additive Manufacturing

Background:

• Selective Laser Melting (SLM) involves rapid heating and cooling, creating high temperature gradients and small melt pools.
• These conditions at the microscale are suitable for atomistic simulations to investigate solidification mechanisms.
• Understanding primary microstructure formation is crucial for optimizing SLM processes and material properties.

Purpose of the Study:

• To investigate microscopic mechanisms of primary microstructure formation during molten metal solidification in SLM.
• To utilize Classical Molecular Dynamics (CMD) simulations for studying solidification phenomena.
• To compare simulation results with experimental data from real SLM-processed materials.

Main Methods:

• Classical Molecular Dynamics (CMD) simulations were employed to model the solidification of 316L austenitic stainless steel.
• The simulations focused on the interaction of the solidification front with substrate defects and newly formed defects.
• Microstructures from CMD simulations were compared with experimental data obtained via Electron Backscatter Diffraction (EBSD) on SLM-produced samples.

Main Results:

• Solidified material inherits substrate defects and forms new ones, such as twin boundaries, influencing the primary microstructure.
• Solidification behavior varies with crystallographic direction and interactions with grain boundaries and defects.
• CMD simulations successfully reproduced key microstructural features observed in EBSD analysis of SLM-processed 316L stainless steel.

Conclusions:

• Large-scale atomistic simulations are capable of reproducing the main microstructural features formed during metallic additive manufacturing by SLM.
• CMD provides insights into the microscopic mechanisms governing microstructure evolution during SLM.
• The study validates the use of atomistic simulations as a powerful tool for understanding and optimizing additive manufacturing processes.