Page 7 - ESAM-1-1

Engineering Science in

Additive Manufacturing

REVIEW ARTICLE
Machine learning in image-based metal

additive manufacturing process monitoring and
control: A review

Jian Wang , Xin Zhang , and Yanglong Lu*

Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and
Technology, Clear Water Bay, Hong Kong SAR, China

Abstract

Metal additive manufacturing (MAM) has transformed the fabrication of intricate,
high-performing components for sectors such as aerospace, automotive, and
healthcare. However, maintaining consistent quality remains a significant challenge
due to the process’s intrinsic complexity and susceptibility to defects. Recent advances
in machine learning (ML), particularly in combination with image-based monitoring
and control, have demonstrated significant potential to address these limitations by
enabling real-time defect detection, process optimization, and adaptive control. By
leveraging techniques such as deep learning and computer vision, ML can extract
actionable insights from the vast amounts of image data generated during MAM
processes. This allows for the accurate identification of defects ranging from porosity
and cracking to thermal distortions while simultaneously predicting anomalies
*Corresponding author: and optimizing process parameters such as laser power, scanning speed, and feed
Yanglong Lu rate. These developments pave the way for closed-loop control systems capable of
(maeylu@ust.hk) dynamically adjusting process conditions to mitigate defects, improve part quality,
Citation: Wang J, Zhang X, Lu Y. and enhance overall process stability. However, significant challenges remain,
Machine learning in image-based including the need for high-quality labeled datasets, computationally efficient
metal additive manufacturing
process monitoring and control: algorithms, and robust generalization across different materials, geometries, and
A review. Eng Sci Add Manuf. process conditions. Addressing these challenges will require the integration of
2025;1(1):8548. domain knowledge, physics-based models, and advanced ML techniques, alongside
doi: 10.36922/esam.8548
the establishment of standardized datasets and evaluation protocols. This review
Received: January 15, 2025 synthesizes current progress and identifies future research directions, emphasizing
Revised: February 14, 2025 the transformative role of ML in advancing MAM toward fully autonomous, intelligent
manufacturing systems.
Accepted: February 19, 2025
Published online: March 6, 2025
Keywords: Metal additive manufacturing; Machine learning; Image-based data; Process
Copyright: © 2025 Author(s). monitoring; Process control
This is an Open-Access article
distributed under the terms of the
Creative Commons Attribution
License, permitting distribution,
and reproduction in any medium, 1. Introduction
provided the original work is
properly cited. Following the advancement in technologies for manufacturing and computer-assisted
Publisher’s Note: AccScience design, additive manufacturing (AM) has attracted considerable attention. AM, also
Publishing remains neutral with known as three-dimensional (3D) printing, is a transformative approach to industrial
regard to jurisdictional claims in
published maps and institutional production that enables the creation of lighter, stronger parts and systems. This
affiliations. innovative process involves slicing complex 3D objects into layers in two dimensions

Volume 1 Issue 1 (2025) 1 doi: 10.36922/esam.8548