Prediction of sintered density of binary W(Mo)alloys using machine learning

Powder metallurgy is the optimal method for the consolidation and preparation of W(Mo)alloys,which exhibit excellent application prospects at high temperatures.The properties of W(Mo)alloys are closely related to the sintered density.However,controlling the sintered density and porosity of these alloys is still challenging.In the past,the regulation methods mainly focused on timeconsuming and costly trial-and-error experiments.In this study,the sintering data for more than a dozen W(Mo)alloys constituted a small-scale dataset,including both solid and liquid phases sintering.Furthermore,simple descriptors were used to predict the sintered density of W(Mo)alloys based on the descriptor selection strategy and machine learning method(ML),where ML algorithm included the least absolute shrinkage and selection operator(Lasso)regression,k-nearest neighbor(k-NN),random forest(RF),and multi-layer perceptron(MLP).The results showed that the interpretable descriptors extracted by our proposed selection strategy and the MLP neural network achieved a high prediction accuracy(R>0.950).By further predicting the sintered density of W(Mo)alloys using different sintering processes,the error between the predicted and experimental values was less than 0.063,confirming the application potential of the model.

Reduction behavior of tungsten oxide with and without scandia doping

As an important powder material for scandate cathode,Sc_(2) O_(3)-doped WO_(3) powder together with hollow spherical WO_(3) for comparison was prepared by spraydrying method.The reduction behavior and kinetics of pure WO_(3) and Sc_(2) O_(3)-doped WO_(3) were studied by temperatureprogrammed reduction(TPR) method.It is found that scandia doping can decrease the reduction activation energy of WO_(3) and thus lower the reduction temperature and increase the reduction rate.Based on the kinetics results,the reduction techniques are presented.The obtained powder has a narrow size distribution in the range of 0.6-0.8 μm.