Transfer learning aid the prediction of sintering densification

Abstract

In powder metallurgy engineering, the master sintering curve (MSC) is crucial for estimating the mechanical properties of sintered products and optimizing sintering process parameters. A rapid evaluation method, that is, a domain-adversarial neural network, is established in the field of sintering to transfer learning from Al2O3 to SiC, concerning the effect of the heating rate on the sintering densification, in which one material (e.g. SiC) lacks an MSC and the material (e.g. Al2O3) has an overabundance of MSCs. In the unsupervised mode, we can roughly predict the densification evolution of SiC based on the MSC data of Al2O3. In the semi-supervised mode, prediction accuracy gradually increases with the increase of Al2O3 data and approaches a certain upper limit. Compared with the traditional sintering density prediction models, the proposed approach can provide an effective and rapid solution to the problem of data scarcity in the sintering field.