3D characterisation of metal powders for additive manufacturing using Nano-CT and deep learning

The geometrical properties of metal powders, the main raw material in additive manufacturing processes, can have a significant impact on the quality of the parts produced. Therefore, it is essential to characterise these powders effectively. Nano-computed tomography (Nano-CT) technology, with its high-resolution 3D measurement capability, is ideally suited to measure the geometrical characteristics of tiny powders. However, the current CT testing methods for metal powders has two main drawbacks. Firstly, it does not take into account the effect of particle adhesion. Second, it cannot achieve high-precision measurements of the internal pores of powders. To address these limitations, we propose a novel testing method for metal powders that utilises Nano-CT technology. The method first obtains highly dispersed powder samples through a specially sample preparation technique. Then, an improved U-shape networks with multi-level supervision are proposed to achieve high-precision segmentation of the internal pores of the powder. Ultimately, this paper achieves high-precision 3D characterisation of metal powders.