Optimizing process parameters for selective laser sintering based on neural network and genetic algorithm

Selective laser sintering (SLS) is an attractive rapid prototyping (RP) technology capable of manufacturing parts from a variety of materials. However, the wider application of SLS has been limited, due to their accuracy. This paper presents an optimal method to determine the best processing parameter for SLS by minimizing the shrinkage. According to the nonlinear and multitudinous processing parameter feature of SLS, the theory and the algorithms of the neural network are applied for studying SLS process parameters. The process is modeled and described by neural network based on experiment. Moreover, the optimum process parameters, such as layer thickness, hatch spacing, laser power, scanning speed, work surroundings temperature, interval time, and scanning mode are obtained by adopting the genetic algorithm based on the neural network model. The optimum process parameters will be benefit for RP users in creating RP parts with a higher level of accuracy.

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