Prediction of line heating deformation for sheet based on SSA-ELM algorithm

Abstract

Abstract Line heating is highly susceptible to plastic deformation leading to structural changes, which seriously affects shipbuilding efficiency. It is extremely important to obtain the precise line heating deformation law. There is much research on plate deformation prediction, but there is still no mature, accurate and fast prediction method. Based on this, it is necessary to research the line heating deformation prediction method. ELM has the advantages of fast learning speed, good generalization performance and strong function approximation ability. To address the poor stability of the regression model due to the randomly generated weights and thresholds of ELM, a combined prediction model (SSA-ELM) based on a squirrel search algorithm (SSA) optimized limit learning machine is proposed. The results show that the relative errors of transverse shrinkage and angular deformation of SSA-ELM are improved by 4.7% and 3.9%. The SSA-ELM prediction model has good regression accuracy and generalization ability. It gives more accurate prediction results in terms of line heating deformation prediction.