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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Machine Learning Informed Hot Isostatic Pressing Parameters for beta Grain Refinement and Porosity Control in Laser Powder Bed Fusion of Ti-6Al-4V.

Author(s)

Amaranth Karra, Nishat Saiyara, Hector Siller

On-Site Speaker (Planned)

Amaranth Karra

Abstract Scope

Laser Powder Bed Fusion (LPBF) has seen a huge surge because of the ability to design and fabricate complex shapes with high precision near-net surface finish. Ti-6Al-4V (Ti64) is a widely used titanium alloy commercially valued for high strength to weight ratio and printability in aerospace and biomedical applications. However, fabrication of Ti64 using LPBF presents challenges such as porosity and columnar beta growing parallel to the build direction, which detrimentally effect the mechanical properties. Hot Isostatic Press (HIP) enhances the material properties by closing internal pores and promoting grain refinement; however, the parameters must be carefully chosen to not distort the final build component. In this work, we aim to use machine learning (ML) informed HIP parameters to understand grain refinement and porosity control in LPBF. The proposed model aims to show that using nominal input parameters, a HIP cycle can be accurately predicted and experimentally verified.

Proceedings Inclusion?

Planned:

Keywords

Additive Manufacturing, Machine Learning, Titanium

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