An intelligent parameter selection system for the direct metal laser sintering process

Abstract

As one of the promising Rapid Prototyping (RP) processes, the Direct Metal Laser Sintering (DMLS) technique is capable of building prototype parts by depositing and melting metal powders layer by layer. Metal powder can be melted directly to build functional prototype tools. During fabrication, four important resulting properties of interest to the users are: the processing time, mechanical properties, geometric accuracy and surface roughness. By adjusting an identified set of process parameters, these properties can be properly controlled. The process parameters involve: the laser scan speed, laser power, hatch density, layer thickness and scan path. But the relationships between these parameters and their resulting properties are quite complicated. In many cases, the effects of different parameters on the resulting properties contradict one another. In this paper, an intelligent system to assist the RP user to choose the optimal parameter settings based on different user requirements is presented. For the accurate prediction of the resulting properties of the laser-sintered metal parts, a method based on the feed-forward neural network (NN) with backpropagation (BP) learning algorithm is described. Through experiments, some input–output data pairs have been identified. After continuous training by using the data pairs, this NN constructs a good mapping relationship between the process parameters and their resulting properties. The system developed can determine the most suitable parameter settings containing the process parameters and predict resulting properties from the database built based on different process requirements automatically. It is very useful to RP users for saving material cost and reducing processing time.