Modeling and predicting the tensile strength of poly (lactic acid)/graphene nanocomposites by using support vector regression

Abstract

According to an experimental dataset under different process parameters, support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization was employed to establish a mathematical model for prediction of the tensile strength of poly (lactic acid) (PLA)/graphene nanocomposites. Four variables, while graphene loading, temperature, time and speed, were employed as input variables, while tensile strength acted as output variable. Using leave-one-out cross validation test of 30 samples, the maximum absolute percentage error does not exceed 1.5%, the mean absolute percentage error (MAPE) is only 0.295% and the correlation coefficient [Formula: see text] is as high as 0.99. Compared with the results of response surface methodology (RSM) model, it is shown that the estimated errors by SVR are smaller than those achieved by RSM. It revealed that the generalization ability of SVR is superior to that of RSM model. Meanwhile, multifactor analysis is adopted for investigation on significances of each experimental factor and their influences on the tensile strength of PLA/graphene nanocomposites. This study suggests that the SVR model can provide important theoretical and practical guide to design the experiment, and control the intensity of the tensile strength of PLA/graphene nanocomposites via rational process parameters.