Machine leaning aided study of sintered density in Cu-Al alloy

The mechanical properties of powder metallurgy (PM) materials are closely related to their density. In this case we demonstrate an approach of utilizing machine-learning algorithms trained on experimental data to predict the sintered density of PM materials. Descriptors were selected from the features including processing parameters, chemical composition, property of raw materials and so on. And the training data are collected by the experimental setup in our lab and the literatures on five kinds of P/M alloys. The multilayer perceptron model (MLP) outperformed other four regression and neutral network models with high coefficient of correlation and low error. The sintered density predicted by MLP model agreed well with the experimental data with a tolerable error less than 0.028, which confirms its capability over P/M materials design procedures. Then the obtained MLP model is used for Cu-9Al P/M alloy to guide selecting the processing parameters to reach the expected sintered density of 0.88. The Cu-9Al powders were fabricated with the predicted parameters including the specific shape factor, particle size, pressing pressure and sintering temperature, and the obtained relative sintered density is 0.885.