增材制造粉末二维射线照片中孔隙缺陷的衰减校正深度学习
Attenuation-adjusted deep learning of pore defects in 2D radiographs of additive manufacturing powders
AI总结：
本文提出结合X射线衰减模型与改进UNet的深度学习孔隙分割方法，通过合成数据预训练、粒子裁剪和理想粒子减去，在二维射线照片上实现增材制造粉末气孔的高通量快速检测。
摘要：
用于增材制造的金属原料粉末中气孔的存在会极大地影响最终增材制造产品。由于当前的孔隙率分析通常需要围绕样品进行完整旋转的长时间X射线计算机断层扫描（XCT），因此存在探索高通量方法的动机——可能使得制造过程中的在线孔隙率分析成为可能。通过对粉末的单张二维射线照片上的孔隙像素进行标注，本工作试图模拟此类未来的高效设置。通过将粒子X射线衰减模型与广泛应用的UNet架构的一种变体相结合，实现了高分割精度；值得注意的是，与基线UNet相比，F1分数提高了11.4%。所提出的孔隙分割通过以下方式实现：1）在合成数据上进行预训练，2）制作紧密的粒子裁剪，以及3）减去由受朗伯-比尔定律启发的距离图生成的无孔隙理想粒子。本文探索了四种图像处理方法，其中最快（但仍未优化）的方法平均在0.014秒内分割一个粒子，F1分数为0.78，而最准确的方法在0.291秒内完成，F1分数为0.87。由于其可扩展性，这些策略可用于实现增材制造金属原料粉末的高通量孔隙率分析。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
