Page 32 - IJAMD-1-1

International Journal of AI

for Material and Design

REVIEW ARTICLE
Machine learning applications for quality

improvement in laser powder bed fusion: A
state-of-the-art review

Jiayi Zhang, Ce Yin, Yiyang Xu, and Swee Leong Sing*

Department of Mechanical Engineering, College of Design and Engineering, National University of
Singapore, Singapore, Republic of Singapore

Abstract

As one of the most popular additive manufacturing methods, laser powder bed
fusion (L-PBF) builds 3D components with complex geometries layer by layer using
alloy powders. This technique has found widespread adoption in various industrial
applications, including biomedical and aerospace fields. However, L-PBF encounters
challenges related to poor process repeatability and inconsistency in fabricated
part quality, which hinder its broader adoption. Various quality improvement
methods have been proposed to address these challenges and achieve high-quality,
reliable parts. Given the abundance of parameters and the intricate phenomena
that occur during the process, machine learning (ML) methods play a critical role
in enhancing the quality of L-PBF, providing an optimum solution for improving
the quality of manufactured parts. This review paper begins with a comprehensive
and straightforward introduction to ML, focusing primarily on different learning
approaches. Subsequently, the paper explores different ML methods applied to
parameter optimization and in situ monitoring, both contributing to enhanced
*Corresponding author: quality control. In parameter optimization, ML is employed to extract relationships
Swee Leong Sing between input parameters and key factors such as melt pool characteristics,
(sweeleong.sing@nus.edu.sg) porosity, and mechanical properties. Shifting the focus to in situ monitoring, the
Citation: Zhang J, Yin C, Xu Y,
Sing SL. Machine learning paper introduces the application of ML in analyzing various sensor data generated
applications for quality improvement throughout the L-PBF process. Accomplished tasks include segmentation, regression,
in laser powder bed fusion: A state- and classification of quality measurement. In summary, this review underscores the
of-the-art review. Int J AI Mater
Design. 2024;1(1):2301. critical role of machine learning in addressing challenges associated with L-PBF,
https://doi.org/10.36922/ijamd.2301 providing an optimal solution for quality enhancement.
Received: November 23, 2023
Accepted: January 8, 2024 Keywords: Additive manufacturing; Laser powder bed fusion; Quality improvement;
Published Online: January 23, 2024
Machine learning; Parameter optimization; In situ monitoring
Copyright: © 2024 Author(s).
This is an Open-Access article
distributed under the terms of the
Creative Commons Attribution
License, permitting distribution, 1. Introduction
and reproduction in any medium,
provided the original work is Additive manufacturing (AM), commonly known as three-dimensional (3D) printing,
properly cited. is a fabrication method where a 3D model is initially created using modeling software.
Publisher’s Note: AccScience Subsequently, the physical object takes shape by stacking multiple layers using program-
publishing remains neutral with controlled data and raw materials. Powder bed fusion (PBF) is an advanced AM
regard to jurisdictional claims in
published maps and institutional technology that enables the selective melting of powder materials at high temperatures.
affiliations. In this process, a heat source follows a predefined path, progressively solidifying the

Volume 1 Issue 1 (2024) 26 https://doi.org/10.36922/ijamd.2301