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Applications of machine learning in metal powder-bed fusion in-process monitoring and control: status and challenges

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  • Yingjie Zhang

    (South China University of Technology)

  • Wentao Yan

    (National University of Singapore)

Abstract

The continuous development of metal additive manufacturing (AM) promises the flexible and customized production, spurring AM research towards end-use part fabrication rather than prototyping, but inability to well control process defects and variability has precluded the widespread applications of AM. To solve these issues, process monitoring and control is a powerful approach. Recently, a variety of monitoring methods have been proposed and integrated with metal AM machines, which enables a large volume of data to be collected during the process. However, the data analytics faces great challenges due to the complexity of the process, bringing difficulties on developing effective models for defects detection as well as feedback control to improve quality. To overcome these challenges, machine learning methods have been frequently employed in the model development. By using machine learning methods, the models can be built based on the collected dataset, while it is not necessary to fully understand the process. This paper reviews the applications of machine learning methods in metal powder-bed fusion process monitoring and control, illuminates the challenges to be solved, and outlooks possible solutions.

Suggested Citation

  • Yingjie Zhang & Wentao Yan, 2023. "Applications of machine learning in metal powder-bed fusion in-process monitoring and control: status and challenges," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2557-2580, August.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:6:d:10.1007_s10845-022-01972-7
    DOI: 10.1007/s10845-022-01972-7
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    References listed on IDEAS

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    Cited by:

    1. Hao Jiang & Zhibin Zhao & Zilong Zhang & Xingwu Zhang & Chenxi Wang & Xuefeng Chen, 2025. "Qualitative and quantitative characterization of powder bed quality in laser powder-bed fusion additive manufacturing by multi-task learning," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2695-2707, April.
    2. Mengxuan Gao & Songmei Yuan & Jiayong Wei & Jin Niu & Zikang Zhang & Xiaoqi Li & Jiaqi Zhang & Ning Zhou & Mingrui Luo, 2024. "Optimization of processing parameters for waterjet-guided laser machining of SiC/SiC composites," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 4137-4157, December.
    3. Zimeng Jiang & Aoming Zhang & Zhangdong Chen & Chenguang Ma & Zhenghui Yuan & Yifan Deng & Yingjie Zhang, 2024. "A deep convolutional network combining layerwise images and defect parameter vectors for laser powder bed fusion process anomalies classification," Journal of Intelligent Manufacturing, Springer, vol. 35(6), pages 2929-2959, August.

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