Nonlinear Deformation Prediction and Compensation for 3D Printing Based on CAE Neural Networks

摘要

Although 3D printing technology has developed rapidly in recent years, there are many problems to be solved. The accuracy of the objects obtained by 3D printing is low compared with subtractive manufacturing methods. There are many factors that affect the errors, such as the shape of the object, the properties of the material, and parameters in the printing process. We takes Digital Light Processing (DLP) 3D printing as an example in this paper and take the dental crowns as the test category. We use the Convolutional Auto-Encoder (CAE) architecture to make prediction and compensation for the error of the 3D models directly. We use a simulation method to obtain the data, and test on nonlinear deformation. The results show that our network has a good ability to approximate nonlinear deformation.