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

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.

Keywords

Machine learning / Additive manufacturing / Biomedical metals / Forward prediction / Inverse optimization / Quality control and monitoring

Acknowledgments

None.

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.

Author contributions

Conceptualization: Yi Mao, Liqiang Wang, Uglov Vladimir, Zhou Jing

Visualization: Yi Mao, Deyu Jiang

Writing-original draft: Yi Mao, Deyu Jiang

Writing-review & editing: All authors

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data

Not applicable.

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