# Principles and Characteristics of Different EDM Processes in Machining Tool and Die Steels
## 4 Research in EDM of Tool Steel
### Modeling and Simulation of EDM Process  
Many modeling and simulation techniques were presented to find the optimal conditions for the EDM process. For example, [PERSON] and [PERSON] [208] proposed a 3D model of flow field with liquid, gas, and solid phases for the machining gap in EDM. The model analyzed the mechanisms of debris and bubble movement in the machining gap during consecutive pulse discharges. Experimental and simulation model results showed that the proposed model was feasible. Much of the debris moves outside the machining gap following the excluded bubbles, representing the main path via which the debris is excluded from the machining gap. The bubble expansion strengthened with increased discharge current and pulse-on time. Figure 12 shows the simulation and observation experimental results of the bubble and debris movement. [PERSON] and [PERSON] [139] applied a support vector machine to develop MRR models and average SR parameter models using support vector regression with three control parameters, namely, current, pulse-on time, and pulse-off time. For accurate model fitting, particle swarm optimization was employed to find the optimal combination of the internal parameters of the support vector machine, including regularization parameter, radius of the loss-insensitive hyper tube, and standard deviation of the kernel function. A high-speed steel specimen was chosen as the workpiece material. Figure 12: (**a**) Simulation and (**b**) observation experimental results of the bubble and debris movement [208]. [PERSON] et al. [126] used the ABAQUS code finite element software to simulate the temperature distribution on the surface of an AISI H13 tool steel workpiece and the tool during a single discharge in the EDM process. Furthermore, [PERSON] et al. [171] presented a multi-scale finite element model (FEM) for the single discharging of ASP2023 tool steel to incorporate the plasma-induced time/space-dependent Gaussian heat flux via a user subroutine. The long-standing numerical singularity of heat flux in EDM modeling was solved using the innovative functions of discharge current. The effects of discharge duration and current on the temperature profiles, crater formation, and dimensions were investigated. [PERSON] et al. [209] studied the characteristics of EDM plasma. A thermal-physical model and a metallographic method were introduced to study the heat flux of the plasma by comparing the boundary of the melted material in the crater. For mold steel 8407, the authors reported that the Gauss heat source was closer to the actual EDM process than the point heat source, circular heat source, and other heat source types. A novel approach for process modeling and optimization of EDM was proposed by [PERSON] and [PERSON] [166]. The study integrated FEM with ANN and genetic algorithms (GA) to improve the prediction accuracy of the model with less dependency on the experimental data. FEM was also used to develop a thermo-physical model for the die sinking EDM process [163], whereas numerical analysis of the single spark operation of the EDM process was carried out considering the two-dimensional (2D) axisymmetric process continuum and an AISI P20 mold steel workpiece. [PERSON] et al. [106] also presented a simulation and model of the EDM process. Temperature fields within the workpiece generated by the superposition of multiple discharges were numerically calculated using a finite difference schema. The characteristics of the discharge for a given operation, i.e., energy transferred onto the workpiece, diameter of the discharge channel, and material removal efficiency, were estimated using inverse identification from the results of the numerical model. The authors reported the variation of volume of material removed per discharge as the operation progressed, as shown in Figure 13.  
[PERSON] et al.