Attenuation-adjusted deep learning of pore defects in 2D radiographs of additive manufacturing powders

TL;DR

This paper presents a novel deep learning approach combining attenuation modeling and UNet architecture to efficiently segment pores in 2D radiographs of metal powders, enabling high throughput porosity analysis for additive manufacturing.

Contribution

It introduces an attenuation-adjusted deep learning method with synthetic pretraining and particle modeling, significantly improving pore segmentation accuracy and speed over baseline methods.

Findings

F1-score increased by 11.4% over baseline UNet

Fastest segmentation in 0.014s with F1-score 0.78

Most accurate segmentation in 0.291s with F1-score 0.87

Abstract

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.