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Review
. 2022 Oct 28;15(21):7598.
doi: 10.3390/ma15217598.

Online Monitoring Technology of Metal Powder Bed Fusion Processes: A Review

Zhuo-Jun Hou  1 , Qing Wang  1 , Chen-Guang Zhao  1 , Jun Zheng  1   2 , Ju-Mei Tian  3 , Xiao-Hong Ge  1 , Yuan-Gang Liu  4
Affiliations
Review

Online Monitoring Technology of Metal Powder Bed Fusion Processes: A Review

Zhuo-Jun Hou et al. Materials (Basel). .

Abstract

Metal powder bed fusion (PBF) is an advanced metal additive manufacturing (AM) technology. Compared with traditional manufacturing techniques, PBF has a higher degree of design freedom. Currently, although PBF has received extensive attention in fields with high-quality standards such as aerospace and automotive, there are some disadvantages, namely poor process quality and insufficient stability, which make it difficult to apply the technology to the manufacture of critical components. In order to surmount these limitations, it is necessary to monitor the process. Online monitoring technology can detect defects in time and provide certain feedback control, so it can greatly enhance the stability of the process, thereby ensuring its quality of the process. This paper presents the current status of online monitoring technology of the metal PBF process from the aspects of powder recoating monitoring, powder bed inspection, building process monitoring, and melt layer detection. Some of the current limitations and future trends are then highlighted. The combination of these four-part monitoring methods can make the quality of PBF parts highly assured. We unanimously believe that this article can be helpful for future research on PBF process monitoring.

Keywords: additive manufacturing; electron beam melting; online monitoring; powder bed fusion; selective laser melting.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 2
Figure 2
Schematic of the PBF process [17].
Figure 1
Figure 1
Overview metal−based additive manufacturing.
Figure 3
Figure 3
Integration of acceleration sensor at the recoating mechanism [20].
Figure 4
Figure 4
Components of powder scanner [23].
Figure 5
Figure 5
Visible light inspection system for powder bed [21].
Figure 6
Figure 6
Image of deposited powder bed with worn coater blade [21].
Figure 7
Figure 7
Powder bed inspection using low–coherence interference imaging in SLM. (a) Optical morphology of powder bed surfaces; (b) Profile scanning of powder materials [28].
Figure 8
Figure 8
Anomaly diagram of powder bed [35].
Figure 9
Figure 9
MSCNN anomaly analysis diagram [35].
Figure 10
Figure 10
Schematic assembly of online process control [37].
Figure 11
Figure 11
Single−channel SLM pool monitoring based on low coherence interference [41].
Figure 12
Figure 12
Line scan and point scan schematics [52].
Figure 13
Figure 13
Influence of scan strategies on grain morphology in EBM [52]. (a) The layer thermal gradients; (b) Interface velocity; (c) Grain morphology distribution under line scan strategy; (d) Grain morphology distribution under point scanning strategy.
Figure 14
Figure 14
Melt layer inspection based on visual imaging. (a) Melt layer image; (b) 3D reconstruction model and powder bed anomaly [6].
Figure 15
Figure 15
Diagram of linking features and process parameters using machine learning methods [62].
Figure 16
Figure 16
Electronic microscope schematic [63].
Figure 17
Figure 17
Ultrasound scan and corresponding X–ray CT scan [67].
See this image and copyright information in PMC

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