3D printing process optimization design method based on intelligent computing technology

3D printing has a great potential in manufacturing design, and it is regarded as a key sign of the industrial revolution. In this paper, we introduce the artificial neural network to predict and optimize the 3D printing process. Firstly, we introduce main characteristics of 3D printing technology, which exploits adhesive materials and the layer by layer printing method to construct 3D objects. Secondly, we discuss how to describe and predict the 3D printing process with the artificial neural network, which is made up of several single feed-forward neural networks and the outputs are integrated together with weighting coefficients. Particularly, each individual neural network is able to compute the mechanical compression strength and open porosity independently. Finally, we construct an experiment using a famous 3D printing machine - Zprinter 450 to print the scaffold prototype. Experimental results demonstrate that the proposed method can predict the 3D printing process with high accuracy.