Dispersion uniformity of titanium alloy/graphene oxide mixed powder based on deep learning image segmentation

The dispersion uniformity of titanium alloy/graphene oxide （GO） mixed powder is crucial for the preparation of high-quality graphene-reinforced titanium matrix composites. The mechanism of GO adsorption onto the surface of titanium alloy particle is revealed by theoretical analysis and a quantitative evaluation method is established based on image segmentation using deep learning and statistics analysis. The results show that the SEM secondary electron images of the mixed powder dried for 12 h after solution stirring mixing exhibit high imaging contrast and strong GO adsorption state. The additional pressure generated by the pressure difference between the inside and outside of the liquid bridge is the dominant part for GO adsorption force onto the surface of titanium alloy particle，which is ten orders of magnitude higher than the force of GO. The U-Net network model demonstrates superior segmentation performance for the mixed powder and GO compared to DeepLabV3+ and PSPNet. Under the optimal training parameters，the segmentation accuracies of U-Net for the mixed powder and GO reach relatively high values of 0.9433 and 0.8774， respectively. The stirring blades shape，stirring speed， and stirring time are optimized by numerical simulation of stirring process and the established quantitative evaluation method. The paddle with three inclined blades is the preferred stirring paddle shape for preparing titanium alloy/GO mixed powder. For the mixed powder containing 0.15% （mass fraction， the same below） GO， the preferred stirring process is 400 r/min for 40 min， where the standard deviation and range of GO content are 0.82% and 2.15%， respectively. For the mixed powder containing 0.30%GO， the preferred stirring process is 300 r/min for 80 min， and the standard deviation and range of the GO content are 1.03% and 3.40%，respectively.