Abstract
Shrinkage, one of the typical phenomena in the selective laser sintering (SLS) process, affects the final dimensional accuracy of SLS products. We investigated the relationship between the shrinkage and the process parameters of SLS in order to improve dimensional accuracy. According to the characteristic of SLS, the following process parameters are considered: layer thickness, hatch spacing, laser power, scanning speed, temperature of working environment, interval time and scanning mode. A neural network model on the relationship between the processing parameters and shrinkage was built on the basis of a series of experiments. The experimental investigation results show that the neural network model is possible to be used to predict the effects of the process parameters on the shrinkage with reasonable accuracy and to analyze the relationship between the shrinkage and the process parameters of SLS quantitatively. So it is suitable to apply neural networks approach to study the SLS process. This model will allow us to produce the SLS parts with the desired quality attributed by selecting the appropriate parameter values prior the processing. This paper proposes a promising approach to improve the accuracy of the SLS part.
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