Advancing intelligent additive manufacturing: Machine learning approaches for process optimization and quality control

Abstract

Additive manufacturing (AM) has revolutionized modern fabrication by enabling complex geometries, material efficiency, and customized production. However, process variability, material inconsistencies, and defect formation remain critical challenges, limiting scalability and industrial adoption. Machine learning (ML) has emerged as a powerful tool to address these limitations by enabling data-driven optimization, defect detection, material property prediction, and real-time process control. This review provides a comprehensive analysis of ML applications in AM, spanning polymers, metals, ceramics, and carbon-based materials, with a focus on process optimization, quality assurance, and predictive modeling. Specifically, this review examines real-time defect detection through vision-based ML techniques, printing parameter optimization using supervised and reinforcement learning, and predictive modeling of material properties-laying the groundwork for deeper exploration of key methodologies such as deep learning and physics-informed models. Key ML methodologies, including deep learning, reinforcement learning, and hybrid physics-informed models, are explored in the context of enhancing print precision, mechanical performance, and functional properties. Despite significant advancements, challenges such as data scarcity, model generalization, and real-time integration into AM workflows persist. Emerging trends, including multimodal sensor fusion, in situ monitoring, and cloud-based predictive analytics, are discussed as potential pathways toward fully autonomous and intelligent manufacturing. By consolidating recent developments and outlining future directions, this review provides essential insights for researchers, engineers, and industry professionals looking to harness ML in AM, facilitating advancements in process efficiency, quality control, and overall manufacturing reliability.

Keywords

Additive manufacturing / Machine learning / Process optimization / Defect detection / Quality assurance / Material property / Real-time monitoring / Data-driven manufacturing

Acknowledgments

None.

Funding

This work was supported by the Technology Development Program (Grant No. S3248116) funded by the Ministry of SMEs and Startups (MSS, Korea), and by the National Research Foundation of Korea (NRF) grant, funded by the Korean government (MSIT; Grant No. RS-2023- 00211636).

Conflict of interest

Im Doo Jung is an Editorial Board Member of this journal, but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Separately, other authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.

Author contributions

Conceptualization: Im Doo Jung, Hayeol Kim

Visualization: Im Doo Jung, Hayeol Kim, Kyung-Hwan Kim

Writing - original draft: All authors

Writing - review & editing: Im Doo Jung, Hayeol Kim

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data

Not applicable.

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