Prediction Mechanical Strength of Sand Mold Samples Fabricated by Three-Dimensional Printing

Abstract

Three-dimensional printing (3DP) was widely applied in sand mold fabrication. One of the major concerns about sand mold is the mechanical strength which determines such defects as sand cut, sand blister, sand explosion, and expansion of the castings. In this study, the linear regression and the BP neural network were applied to predict the bending strength of 3DP samples. Orthogonal experiments were designed to obtain data for training the prediction models. Three-point bending test was used to characterize the actual bending strength. The results showed that the sample weight, resolution X and layer thickness are significant factors while the activator content, recoater speed and sample location are not significant. The maximum error of linear regression is 11.2%, which is very big. A three-layer BP neural network with 6 input nods, 14 hidden nods and 1 output nod was proposed to predict the bending strength of 3DP samples, and the maximum error is 4.3%, which is lower than that of linear regression. So it is more valuable in practical applications.