Quantification and prediction of solidification textures under additive manufacturing conditions

Area of Science:

• Materials Science
• Metallurgy
• Computational Modeling

Background:

• Crystallographic textures significantly influence anisotropic properties of polycrystalline metallic alloys made via additive manufacturing (AM).
• Existing methods struggle to quantify texture accurately due to inherent random grain orientation fluctuations in AM processes.

Purpose of the Study:

• To introduce a novel statistical method for accurately quantifying crystallographic textures in AM.
• To enhance the prediction of anisotropic properties and understand texture formation mechanisms in AM.

Main Methods:

• Developed a statistical method extending Z-scoring to a dynamical regime for texture quantification.
• Applied the method to laser and resolidification of AlSi thin films.
• Integrated phase-field modeling with the statistical method for inferring solid-liquid interface properties.

Main Results:

• The statistical method accurately quantifies texture degree despite random grain orientations in AM.
• Phase-field modeling revealed that interface free-energy anisotropy predominantly controls 〈110〉-dominated textures in AlSi thin films.
• The method, combined with modeling, successfully infers anisotropic interface properties, validated against atomistic simulations.

Conclusions:

• The developed statistical method significantly improves tools for quantifying and predicting AM crystallographic textures.
• The study provides fundamental insights into the physical mechanisms governing texture formation and grain competition during rapid AM solidification.
• This work bridges quantitative texture analysis with predictive modeling for AM materials.