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Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting

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High-throughput alloy and process design for metal additive manufacturing.

Sofia Sheikh¹, Brent Vela¹, Pejman Honarmandi¹

¹Department of Materials Science and Engineering, Texas A&M University, College Station, TX USA.

Npj Computational Materials

|June 16, 2025

Summary

This study developed a computational framework to predict alloy printability for additive manufacturing (AM). It uses deep learning to rapidly assess defects, enabling efficient novel alloy design.

Keywords:

Engineering Metals and alloys

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Production of Single Tracks of Ti-6Al-4V by Directed Energy Deposition to Determine the Layer Thickness for Multilayer Deposition

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Area of Science:

Materials Science
Computational Engineering
Additive Manufacturing

Background:

Conventional engineering alloys often lack suitability for additive manufacturing (AM).
Assessing alloy printability requires complex analysis of composition and processing parameters.
Experimental evaluation is time-consuming, necessitating high-throughput computational methods.

Purpose of the Study:

To introduce a computational framework for assessing alloy printability in AM.
To accelerate the evaluation of process-induced defects like lack-of-fusion, balling, and keyholing.
To facilitate the design of novel alloys optimized for AM.

Main Methods:

Integration of material properties, processing parameters, and thermal models.
Utilizing three thermal models to predict melt pool profiles.
Development of a deep learning surrogate model for accelerated printability assessment.
Validation using printability maps for the CoCrFeMnNi system.

Main Results:

The framework accurately assesses printability and predicts defects.
A deep learning model speeds up assessment by 1000x without accuracy loss.
Printability maps were generated for the CoCrFeMnNi system.
Exploration of printable alloys within the high-entropy alloy space was achieved.

Conclusions:

The developed framework efficiently navigates large alloy design spaces for AM.
Probabilistic printability maps offer insights into defect likelihood and uncertainty.
This approach enhances the design of new alloys for additive manufacturing.