Modeling of processing parameters for the cutting of rolled tungsten plates via abrasive water jet

Abstract

Tungsten is a promising candidate for plasma-facing material in tokamaks. This study aims at determining whether the cut-surface roughness of rolled tungsten plates (RTPs) can be improved using an abrasive water jet (AWJ) through tests combined with modeling of the cutting-process parameters. Based on the key factors affecting the cut-surface roughness, the magnitude and type of test factors were increased, compared with the previous study. A batch test was designed and conducted to investigate the use of an AWJ to cut an RTP and obtain more comprehensive sample data. In addition, the multivariate linear regression method was employed to establish a multivariate quadratic response surface regression model of the relationship between the cutting-process parameters and the cut-surface roughness based on the roughness sample data. Moreover, a backpropagation (BP) artificial neural network (ANN) model was developed based on the cutting-process parameters to predict the cut-surface roughness. The two models were compared and experimentally validated, which showed that the BP ANN model exhibits relatively high accuracy. Thus, the established model can help to improve cut-surface quality and cutting efficiency.