The presence of gas pores in metal feedstock powder for additive manufacturing greatly affects the final AM product. Since current porosity analysis often involves lengthy X-ray computed tomography (XCT) scans with a full rotation around the sample, motivation exists to explore methods that allow for high throughput -- possibly enabling in-line porosity analysis during manufacturing. Through labelling pore pixels on single 2D radiographs of powders, this work seeks to simulate such future efficient setups. High segmentation accuracy is achieved by combining a model of X-ray attenuation through particles with a variant of the widely applied UNet architecture; notably, F1-score increases by $11.4\%$ compared to the baseline UNet. The proposed pore segmentation is enabled by: 1) pretraining on synthetic data, 2) making tight particle cutouts, and 3) subtracting an ideal particle without pores generated from a distance map inspired by Lambert-Beers law. This paper explores four image processing methods, where the fastest (yet still unoptimized) segments a particle in mean $0.014s$ time with F1-score $0.78$, and the most accurate in $0.291s$ with F1-score $0.87$. Due to their scalable nature, these strategies can be involved in making high throughput porosity analysis of metal feedstock powder for additive manufacturing.

翻译：增材制造金属原料粉末中的气孔存在显著影响最终AM产品质量。由于当前孔隙率分析通常涉及耗时较长的X射线计算机断层扫描（XCT），需对样品进行完整旋转扫描，因此有必要探索能够实现高通量检测的方法——这可能为制造过程中的在线孔隙率分析提供可能。本研究通过对粉末单张二维射线图像中的孔隙像素进行标注，旨在模拟此类未来高效检测方案。通过将粒子X射线衰减模型与广泛应用的UNet架构变体相结合，实现了高精度分割；值得注意的是，其F1分数较基准UNet提升了$11.4\%$。所提出的孔隙分割方法基于以下三项关键技术实现：1）在合成数据上进行预训练，2）制作紧密的粒子裁剪图像，3）通过受朗伯-比尔定律启发的距离图生成无孔隙理想粒子并进行图像差分。本文探究了四种图像处理方法，其中最快（尚未优化）的方法单粒子平均分割耗时$0.014s$且F1分数达$0.78$，而最精确的方法耗时$0.291s$且F1分数达$0.87$。得益于其可扩展特性，这些策略有望应用于增材制造金属原料粉末的高通量孔隙率分析。