High-dynamic-range 3D measurement for E-beam fusion additive manufacturing based on SVM intelligent fringe projection system

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10 Citations (Scopus)


Electron Beam Metal Additive Manufacturing (EBAM) has been developed over recent years because of its advantages in manufacturing internal features and complex structures with relatively high productivity. The process proceeds by layer by layer melting and re-solidification of metal powder utilising an electron beam energy source. Following solidification of the build layer, the surface becomes 'shiny' with high reflectivity which makes in-process inspection of the surface of the build layer using fringe projection, difficult. To address this issue, a novel intelligent fringe projection technique using a support-vector-machine (SVM) algorithm is proposed to measure the 3D topography of high dynamic range surfaces on a layer by layer basis within the EBAM machine. To facilitate the SVM implementation a range of EBAM manufactured surfaces are utilised as samples for training and classification. The training measurements are based on different exposure times and saturated pixels are utilised as feature vectors to predict tested samples categories. Training errors are evaluated, and the correct recognition rate is 91%, which indicates the proposed training method can effectively predict the categories of the surfaces. Examples of melting edge swelling and powder bed inspections during a part build are used to demonstrate the system capability for inspection high dynamic range measurement within the EBAM machine. The whole inspection process lasts less than 5 s with two measurements which minimises the time penalty for the manufacturing process. Experimental results showed that the powder and the melting surface defects could be efficiently inspected using the proposed technology and the measurement result could be fed back to the build process to improve the processing quality.

Original languageEnglish
Article number034002
Number of pages15
JournalSurface Topography: Metrology and Properties
Issue number3
Early online date17 Jun 2021
Publication statusPublished - 1 Sep 2021


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