Prediction of Properties of Cu-15Ni-8Sn Alloys Based on Least Square Support Vector Machines

Abstract

A new model based on least square support vector machines (LSSVM) and capable of forecasting mechanical and electrical properties of Cu-15Ni-8Sn alloys has been proposed. Data mining and artificial intelligence techniques of copper alloys are used to examine the forecasting capability of the model. In order to improve predictive accuracy and generalization ability of LSSVM model, leave-one-out-cross-validation (LOOCV) technique is adopted to determine the optimal hyper-parameters of LSSVM automatically. The forecasting performance of the LSSVM model and the artificial neural network (ANN) has been compared with the experimental values. The result shows that the LSSVM model provides slightly better capability of generalized prediction compared to ANN. The present calculated results are consistent with the experimental values, which suggest that the proposed LSSVM model is feasible and efficient and is therefore considerd to be a promising alternative method to forecast the variation of the hardness and electrical conductivity with aging temperature and aging time.