Part-scale microstructure prediction for laser powder bed fusion Ti-6Al-4V using a hybrid mechanistic and machine learning model

Whitney, Bonnie C.; Spangenberger, Anthony G.; Rodgers, Theron M.; Lados, Diana A.

doi:10.1016/j.addma.2024.104500

Title: Part-scale microstructure prediction for laser powder bed fusion Ti-6Al-4V using a hybrid mechanistic and machine learning model

Journal Article · 20 October 2024 · Additive Manufacturing

DOI: https://doi.org/10.1016/j.addma.2024.104500 · OSTI ID:2498468

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^[3]; Lados, Diana A. ^[2]

Worcester Polytechnic Institute, MA (United States); Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Worcester Polytechnic Institute, MA (United States)
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Laser powder bed fusion (LPBF) Ti-6Al-4V is widely studied for use in structural applications in aerospace and medical industries, but mechanical anisotropy and microstructural inhomogeneity prohibits its wider adoption. Although successful microstructure prediction models have been developed, a remaining challenge is their limited integration across length/time scales and validation by experimental studies. Here, this work proposes a physics-augmented machine learning surrogate model to unite predictions of LPBF temperature, β phase morphology and texture, and α/α’ formation into a single framework that is calibrated and validated with experiments. First, a phase field (PF) model of the martensitic β→α’ transformation is developed and calibrated using data from in-situ synchrotron cyclic heating/cooling studies quantifying the variation of α phase fraction with time. In parallel, an established finite difference-Monte Carlo (FDMC) model predicts the part-scale temperature profile and β grain formation during solidification. A dataset is developed using LPBF cyclic temperature descriptors from the FDMC model as inputs and corresponding α/α’ phase fraction and width from the PF model as outputs. Five machine learning (ML) regression models are tested and optimized, having mean absolute error in testing ≤ 4 %, and the k-nearest neighbors (KNN) model is selected as the best performing. The KNN model is called at the nodal level during post-processing of the FDMC model to replace and downscale the response of the PF model. The combined agility and accuracy of the hybrid FDMC-ML model enables part-scale microstructure predictions that can be further used for property predictions to accelerate AM process optimization.

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Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0003525

OSTI ID:: 2498468

Report Number(s):: SAND--2025-00168J

Journal Information:: Additive Manufacturing, Journal Name: Additive Manufacturing Journal Issue: 94 Vol. 94; ISSN 2214-8604

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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36 MATERIALS SCIENCE
additive manufacturing
in-situ synchrotron X-ray diffraction
microstructure simulations
phase transformation
titanium

Title: Part-scale microstructure prediction for laser powder bed fusion Ti-6Al-4V using a hybrid mechanistic and machine learning model

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