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 a layer by layer melting and re-solidification of metal powder by 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 build surface layer using fringe projection difficult. To address this issue a novel intelligent fringe projection technique using support-vector-machine (SVM) algorithm is proposed to measure the 3D topography of high dynamic range surface 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 measurement 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 seconds 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.