A novel hybrid improved hunger games search optimizer with extreme learning machine for predicting shrinkage of SLS parts

Abstract

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. With the development of intelligent optimization algorithms, they are applied in various disciplines for solving complex problems. Hence, for reducing the shrinkage of parts and overcoming the limitation in the optimization of the process parameters, this paper proposes a novel hybrid improved Hunger Games Search algorithm (HGS) with extreme learning machine (ELM) model for predicting the shrinkage of parts. Firstly, the orthogonal experiments were conducted based on the five key process parameters, the obtained parts datasets were divided into the training set and test set. Secondly, the Cube mapping and refracted opposition-based learning strategies are adopted to increase the convergence speed and solution accuracy of HGS. In addition, the regression prediction model was constructed with the improved HGS(IHGS) and ELM, and this model is trained using the training set. Finally, the test set is used to evaluate the trained model and find the optimal combination of process parameters with the lowest shrinkage of parts. The experimental results suggest that the 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.