Effect of sintering on the relative density of Cr-coated diamond/Cu composites prepared by spark plasma sintering

Cr-coated diamond/Cu composites were prepared by spark plasma sintering. The effects of sintering pressure, sintering temperature, sintering duration, and Cu powder particle size on the relative density and thermal conductivity of the composites were investigated in this paper. The influence of these parameters on the properties and microstructures of the composites was also discussed. The results show that the relative density of Cr-coated diamond/Cu reaches ~100% when the composite is gradually compressed to 30 MPa during the heating process. The densification temperature increases from 880 to 915℃ when the diamond content is increased from 45vol% to 60vol%. The densification temperature does not increase further when the content reaches 65vol%. Cu powder particles in larger size are beneficial for increasing the relative density of the composite.

Fabrication and sintering behavior of high-nitrogen nickel-free stainless steels by metal injection molding

High-nitrogen nickel-free stainless steels were fabricated by the metal injection molding technique using high nitrogen alloying powders and a mixture of three polymers as binders.Mixtures of metal powders and binders with various proportions were also investigated, and an optimum powder loading capacity was determined as 64vol%.Intact injection molded compacts were successfully obtained by regulating the processing parameters.The debinding process for molded compacts was optimized with a combination of thermo-gravimetric analysis and differential scanning calorimetry analysis.An optimum relative density and nitrogen content of the specimens are obtained at 1360℃,which are 97.8%and 0.79wt%,respectively.

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.