Page 93 - MSAM-2-3

Materials Science in

Additive Manufacturing

ORIGINAL RESEARCH ARTICLE
Additive manufacturing: Application and

validation of machine learning-based
process-structure-property linkages in Ti-6Al-4V

Xi Gong, Willem Groeneveld-Meijer, and Guha Manogharan*

Department of Mechanical Engineering, Pennsylvania State University, PA, 16801, USA

Abstract

In the field of metal additive manufacturing (AM), various processes and heat
treatments can yield unique grain morphologies, thereby influencing material
properties and machining behavior. In this study, a novel workflow using a machine
learning-based approach that combines statistical descriptors of textured AM-process
induced microstructure, cutting force model (as a material response), and a data-mining
method is established. It is proven to be a valid method for creating process-structure-
property linkages for metal AM. This study focuses on two highly varied metal AM
processes: Powder bed fusion (PBF, e.g., laser PBF and electron beam PBF) and directed
energy deposition (DED, e.g., wire-fed plasma-directed energy deposition). The study
also accounted for the effects of post-AM heat treatment and build orientation. It was
found that the accuracy of material behavior predictions is highly correlated with AM
processing conditions, building orientations, and machining conditions. Specifically,
*Corresponding author: while initially applying PBF training data to DED samples resulted in a 15% root mean
Guha Manogharan square prediction error, this error was subsequently reduced to <1% through cross-
(gum53@psu.edu)
training using combined microstructure training data sets. This discrepancy could be
Citation: Gong X, Groeneveld- attributed to the significantly different thermal cycling conditions in L-PBF and DED,
Meijer W, Manogharan G, 2023, which resulted in highly varied textured microstructures. Residual stresses generated
Additive manufacturing: Application
and validation of machine learning- during AM processing and the selection of machining parameters exert the highest
based process-structure-property impact on the machining behavior. The implications of these findings extend to the
linkages in Ti-6Al-4V. Mater Sci Add use of statistically descriptive microstructures for various AM processing conditions
Manuf, 2(3): 0999.
https://doi.org/10.36922/msam.0999 and build orientations in computational methods and other machining learning
approaches.
Received: May 25, 2023
Accepted: September 24, 2023
Keywords: Additive manufacturing; Machine learning; Process-structure-property
Published Online: September 29, linkage; Ti-6Al-4V
2023
Copyright: © 2023 Author(s).
This is an Open-Access article
distributed under the terms of the
Creative Commons Attribution 1. Introduction
License, permitting distribution,
and reproduction in any medium, Metal additive manufacturing (AM) technologies offer an economical means of
provided the original work is producing highly complex and customized parts with efficient material utilization,
properly cited. particularly suitable for low-volume production . Two major classes of AM processes are
[1]
Publisher’s Note: AccScience employed for titanium alloys: powder bed fusion (PBF) and directed energy deposition
Publishing remains neutral with (DED). Within a given part (based on build orientation), the microstructure and
regard to jurisdictional claims in
published maps and institutional mechanical properties exhibit unique characteristics, strongly influenced by the chosen
affiliations. AM process, processing conditions, and subsequent post-AM treatments. In laser PBF,

Volume 2 Issue 3 (2023) 1 https://doi.org/10.36922/msam.0999