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Journal of Intelligent Manufacturing

Erschienen in:

10.06.2022

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

verfasst von: Yingjie Zhang, Wentao Yan

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 6/2023

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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.

Vorheriger Artikel A systematic literature review on recent trends of machine learning applications in additive manufacturing

Nächster Artikel A novel vision-based method for loosening detection of marked T-junction pipe fittings integrating GAN-based segmentation and SVM-based classification algorithms

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Titel: Applications of machine learning in metal powder-bed fusion in-process monitoring and control: status and challenges
verfasst von: Yingjie Zhang
Wentao Yan
Publikationsdatum: 10.06.2022
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 6/2023
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-022-01972-7

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