Optimization of FDM process parameters for dual extruder 3d printer using Artificial Neural network

Abstract

Abstract Fused Deposition Modelling is one of the most widely used processes of additive manufacturing or 3D printing. The FDM process of 3D printing deposits material in the form of a continuous flow, layer-by-layer to make objects. As the FDM-based products are used in various fields it becomes important to look after the mechanical aspects, part quality, and the economical aspect of FDM 3D printing and hence optimize the necessary process parameters. In this study, critical process parameters like layer thickness, air gap, raster width, build orientation, raster angle, and the number of contours is optimized for enhancing the properties of FDM printed part such as tensile strength surface roughness, and build time. The material used for 3D printing is polylactic acid (PLA). The task of training the data sets and optimizing them was accomplished by using function approximation of Artificial Neural Network. ANN can predict experimental data with a coefficient of correlation R = 0.9981,0.9984,0.99837 subsequently for tensile strength, Build time, and surface roughness and root mean square error as 0.5543, 0.578 and 0.241 for three outputs. Further, it is revealed that build orientation is the most important parameter for optimum results.