AccScience Publishing / ESAM / Volume 1 / Issue 4 / DOI: 10.36922/ESAM025440031
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REVIEW ARTICLE

Machine learning-driven additive manufacturing of biomedical metals: A review of forward prediction, inverse optimization, and quality control

Yi Mao1 ,  Deyu Jiang1 ,  Uglov Vladimir2 ,  Zhou Jing3* ,  Liqiang Wang1*
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1 State Key Laboratory of Metal Matrix Composites, School of Material Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
2 Laboratory of NanoElectroMagnetics, Institute for Nuclear Problems, Belarusian State University, Minsk, Belarus
3 Department of Anatomy, Youjiang Medical University for Nationalities, Baise, Guangxi, China
ESAM 2025, 1(4), 025440031 https://doi.org/10.36922/ESAM025440031
Received: 29 October 2025 | Revised: 24 November 2025 | Accepted: 26 November 2025 | Published online: 5 December 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Additive manufacturing (AM) for biomedical metals presents revolutionary opportunities for producing personalized, complex structured biomedical components. However, the high nonlinearity and complexity of the manufacturing process pose significant challenges to the performance consistency of biomedical metals. Traditional trial-and-error approaches and experience-based optimization methods are increasingly inadequate for meeting the demands of high-reliability medical applications. In recent years, machine learning (ML) has emerged as a powerful data-driven tool, deeply integrating into every stage of AM for biomedical metals and providing a driving force for its intelligent transformation and upgrading. This review outlines three key applications of ML in biomedical metal AM: at the property prediction stage, ML enables forward prediction of performance characteristics by establishing precise mapping relationships between process parameters and macrostructure quality, microstructure, and mechanical/functional properties; at the process optimization level, ML-driven inverse optimization algorithms efficiently navigate high-dimensional parameter spaces to achieve both single-objective perfection and multi-objective balancing; at the quality monitoring and control level, ML enables real-time diagnosis of manufacturing defects and even closed-loop adaptive control by integrating multiple in situ sensor data. This review explores how ML can facilitate the biomedical metals during the AM process and outlines its future development toward fully integrated intelligent design and manufacturing processes.

Graphical abstract
Keywords
Machine learning
Additive manufacturing
Biomedical metals
Forward prediction
Inverse optimization
Quality control and monitoring
Funding
The authors acknowledge the financial supports from the National Key Research and Development Program of China (Grant No. 2024YFE0109000), the National Natural Science Foundation of China (Grant Nos. 52274387, 52311530772), the Medical-Engineering Cross Foundation of Shanghai Jiao Tong University (Grant No. YG2024LC04), and the Fundamental Research Funds for the Central Universities (Grant No. YG2023QNA21).
Conflict of interest
The authors declare that they have no competing interests.
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Engineering Science in Additive Manufacturing, Electronic ISSN: 3082-849X Published by AccScience Publishing
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