Support Vector Regression Prediction of Porosity of Porous NiTi Alloy by Self-Propagation High-Temperature Synthesis

Abstract:

Based on the experimental dataset, the support vector regression (SVR) combined with particle swarm optimization (PSO) for parameter optimization, is proposed to establish a model for estimating the porosities of NiTi alloys synthesized by self-propagation high-temperature synthesis (SHS) approach under different process parameters, including temperature, particle size and green density. The prediction results indicate that the mean absolute percentage error (MAPE) achieved by SVR is smaller and more accurate than that of back-propagation neural network (BPNN) for identical training and test samples, reflecting the prediction ability of SVR is superior to that of BPNN; MAPE predicted by leave-one-out test of SVR (SVR-LOOCV) is also slightly better than that of BPNN, and the correlation coefficient (R2) reaches 0.999. Therefore it is demonstrated that SVR is a promising and practical technique to estimate the porosity of porous NiTi alloy synthesized under different SHS process parameters, and can provide a reasonable guidance for the SHS of porous NiTi theoretically