Abstract
RP(Rapid Prototyping) technology is gaining its popularity in building a prototype in all industries. SLS(Slective Laser Sintering) is one of RP technologies, which is focused on tooling processes as well as three dimension solid model. There are several factors, the length and the cross-sectional area of a part, that have an effect on build setup in SLS process. In this paper, the computation on geometrical relationship is used to slice STL file and to estimate these factors. Based on these values, the build setup parameters such as the heating temperature, the laser power, and the powder cartridge feed rate are determined by neural network approaches. The test results show that the computation time is saved and the neural network approach is able to apply to get the optimal parameters of build process within an acceptable error rate.
