Attenuation-adjusted deep learning of pore defects in 2D radiographs of additive manufacturing powders
This is an incremental improvement for additive manufacturing quality control, enabling faster porosity analysis compared to traditional X-ray computed tomography.
This work tackled the problem of detecting gas pores in metal feedstock powder for additive manufacturing using 2D radiographs, achieving an F1-score increase of 11.4% over a baseline UNet, with the fastest method segmenting a particle in 0.014s at F1-score 0.78 and the most accurate in 0.291s at F1-score 0.87.
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.