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.

电子束金属增材制造（EBAM）因其在制造内部特征和复杂结构方面具有较高的生产率优势，近年来得到了发展。该过程通过利用电子束能源逐层熔化和重新固化金属粉末来进行。构建层固化后，表面变得“有光泽”并具有高反射率，这使得使用条纹投影对构建层表面进行过程中检查变得困难。为了解决这个问题，提出了一种使用支持​​向量机 (SVM) 算法的新型智能条纹投影技术，以在 EBAM 机器内逐层测量高动态范围表面的 3D 地形。为了促进 SVM 的实施，一系列 EBAM 制造的表面被用作训练和分类的样本。训练测量基于不同的曝光时间，饱和像素用作特征向量来预测测试样本类别。对训练误差进行了评估，正确识别率为91%，表明所提出的训练方法可以有效地预测表面的类别。零件制造期间熔化边缘膨胀和粉末床检查的示例用于证明系统在 EBAM 机器内检查高动态范围测量的能力。整个检查过程持续不到 5 秒，有两次测量，最大限度地减少了制造过程的时间损失。