IHGS-ELM for predicting shrinkage of SLS parts
What is it about?
The material constriction is one of the important factors that influence the forming accuracy of selective laser sintering (SLS). Currently, in order to reduce the shrinkage and improve the quality of products, the optimal combination of machining process parameters is mainly determined by numerous experiments. This often takes valuable time and costs a lot, but the results are mediocre. Hence, we propose a novel hybrid improved Hunger Game Search algorithm (HGS) with extreme learning machine (ELM) model for predicting the shrinkage of parts.
Why is it important?
The experimental results suggest that the proposed in this paper IHGS-ELM model proposed in this study has high forecasting precision, with the R2 and RMSE are only 0.9124 and 0.2433, respectively. This model can guide the laser sintering process of polyether sulfone (PES) powder.