# Interaction of Machining Parameters on MRR of Sintered NdFeB Processed by EDM-Milling
## 1 Introduction
[23] used the hybrid Taguchi grey approach to study the machining effect of Ti6 Al4V by wire EDM. It was concluded that interactions of pulse on time versus pulse off time and peak current versus pulse off time on MRR and SR were significant according to the constructed contour plots and their analysis. The significance of the interaction effect of factors on the machining effect could also be determined by ANOVA. [PERSON] et al. [24] carried out magnetic field-assisted microfabrication EDM of AMC materials, and it was shown that the interaction of voltage and feed rate had a more significant effect on MRR based on the ANOVA results. [PERSON] et al. [25] proposed a linear regression model with the inclusion of two-way interactions based on ANOVA to optimally select the process parameters in Micro-EDM Drilling/Milling. The ANOVA-based results revealed that three interaction terms--pulse frequency and current, pulse frequency and regulation gain, and regulation gain and servo voltage--show significant effects on the MRR besides the main effect. [PERSON] et al. [26] found that the interaction of voltage and powder concentration had a significant effect on the taper angle, as determined by ANOVA, in an experimental investigation for drilling micro-holes using the Al2O3 powder-mixed micro-EDM technique. Researchers also used methods such as calculating factor effect values and comparing prediction errors of the regression model to study the interaction of factors on the machining effectiveness of EDM. [PERSON] et al. [27] employed [PERSON]'s standard orthogonal array to design experiments and studied the electrode wear of the hot work tool steel 1.2714. Factor effects were calculated, and it was concluded that the interaction between engaging time and pre-EDM roughing had the least effect on electrode wear according to the values of the factor effects. [PERSON] et al. [28] investigated the effect of process parameters on the MRR of Al-Mg\({}_{2}\)Si by powder-mixed EDM and validated experimental results by the RSM-generated adaptive neuro-fuzzy inference system (ANFIS) model. It was concluded that current-voltage and current-pulse on time had the most significant interaction effect on the MRR according to the drawn three-dimensional ANFIS model of the MRR. [PERSON] et al. [29] used [PERSON]'s experimental method to study the variation of the black layer of the electrode with the interaction of the parameters and found it inconsistent by comparing the additive model-predicted values with the experimental values. It was shown that there was a significant interaction between the parameters, and the black layer on the electrode varied with the interaction of the parameters. [PERSON] et al. [30] used [PERSON]'s method to study the effect of workpiece hardness and each process parameter on the MRR of alloy steels machined by EDM. The regression model of MRR was established, and the significance of each item within the model was determined by comparing the prediction error of the established model. The results showed that the workpiece hardness interacted with all other parameters except the ram cycle. Since the high hardness and brittleness of sintered NdFeB are well-suited for EDM machining, and the analytical parameters selected in this study are the main interaction influences on the efficiency of EDM machining, the importance of analytical parameter interactions is crucial for EDM machining of sintered NdFeB. In addition, there is less study on the trend of change in the significance of the interaction of processing parameters on the effect of EDM processing and its causes. Therefore, in this study, interaction curves are proposed and plotted to more intuitively show the trends in the effect of two-factor interaction on MRR and the significance of the two-factor interaction. Based on these interaction curves, the interaction effects of factors on MRR during EDM milling of sintered NdFeB were investigated, and the causes were analysed through the interaction trends.