Prediction of Microstructure for AISI316L Steel from Numerical Simulation of Laser Powder Bed Fusion

Area of Science:

• Materials Science
• Additive Manufacturing
• Computational Materials Engineering

Background:

• Laser powder bed fusion (L-PBF) requires precise control over microstructure for optimal component properties.
• Predicting microstructure and microhardness is crucial for industrial L-PBF applications.
• Numerical simulations offer a pathway to estimate solidification data for microstructure prediction.

Purpose of the Study:

• To apply a numerical model for simulating L-PBF processing of 316L stainless steel.
• To estimate microstructure cellular arm spacing and microhardness using simulation data.
• To validate the predictive capability of the numerical model against experimental results.

Main Methods:

• Developed and applied a numerical model to simulate L-PBF of single scan tracks.
• Extracted temperature gradient and cooling rate data from the melt pool.
• Estimated cellular arm spacing and microhardness based on simulation outputs.
• Compared estimated values with experimental measurements.

Main Results:

• The numerical model successfully simulated L-PBF of 316L stainless steel under varying parameters.
• Estimated microstructure cellular arm spacing and microhardness showed good agreement with experimental data.
• Validation confirmed the model's accuracy in predicting key material characteristics.

Conclusions:

• Numerical modeling provides a reliable method for predicting microstructure and microhardness in L-PBF components.
• This approach serves as a valuable tool for optimizing L-PBF processes.
• Accurate prediction enables tailoring of final component properties for specific applications.