关键词: 两相流, 颗粒, 奥克托今造型粉, 原位测量, 深度学习

Abstract:

To realize online morphology information measurement of HMX molding powders, the particle imaging probe and acquisition system were developed. Experimental studies were conducted to capture the images of suspended particles of molding powders. Using Mask RCNN as framework, an image processing method based on improved Swin Transformer was proposed, and the CA-Swin Transformer structure was proposed by connecting channel attention module (CAM) and the window-based multi-head self-attention (W-MSA) in parallel, which was utilized to reasonably allocate the attention degree of image channels. The particle recognition network (PRNet) was further established by combining the proposed feature enhancement module (FEM) with CA-Swin Transformer, effectively improving the recognition accuracy of particle images. The PRNet was trained and tested with the labeled image dataset of HMX molding powders. The obtained results showed that the AP, AP₅₀ and AP₇₅ of PRNet reached 62.3%, 84.4% and 72.5%, respectively. The relative errors of recognized feature particle sizes D₁₀, D₅₀, D₉₀ and D_max relative to manual labeling were -4.788%, -0.770%, -0.272% and 0.313%, respectively, outperforming baseline network Mask RCNN and its variant with the Swin Transformer backbone. Moreover, PRNet exhibited a better recovery ability for the occluding part of overlapping particles. The absolute relative errors of circularity, Feret diameter and aspect ratio of overlapping particles relative to manual labeling were less than 8%, 4% and 5%, respectively.

Key words: two-phase flow, particles, Octogen (HMX) molding powders, in-situ measurement, deep learning