Can Cogun

This study examines the mechanical, thermal, and electrical properties of copper tool electrodes processed via Equal Channel Angular Pressing (ECAP), with a specific focus on their performance in Electrical Discharge Machining (EDM) applications. A novel Crystal Plasticity Finite Element Method (CPFEM) framework is employed to model anisotropic slip behavior and microscale deformation mechanisms. The primary objective is to elucidate how initial crystallographic orientation influences hardness, thermal conductivity, and electrical conductivity. Simulations are performed on single-crystal copper for three representative Face Centered Cubic (FCC) orientations. Using an explicit CPFEM model, the study examines texture evolution and deformation heterogeneity during the ECAP process of single-crystal copper. The results indicate that the single-crystal orientation exhibits the highest Taylor factor and the most homogeneous distribution of plastic equivalent strain (PEEQ), suggesting enhanced resistance to plastic flow. In contrast, the single-crystal orientation displays localized deformation and reduced hardening. A decreasing Taylor factor correlates with more uniform slip, which improves both electrical and thermal conductivity, as well as machinability, by minimizing dislocation-related resistance. These findings make a novel contribution to the field by highlighting the critical role of crystallographic orientation in governing slip activity and deformation pathways, which directly impact thermal wear resistance and the fabrication efficiency of ECAP-processed copper electrodes in EDM.

This study examines the use of machine learning (ML) techniques to optimize the basic machining parameters and protrusion dimensions that affect tool shape degeneration in die-sinking electric discharge machining (EDM). The primary objective is to decrease errors and enhance prediction and optimization effectiveness. This study introduces a completely novel tool geometry model aimed at minimizing tool shape degeneration, which, to our knowledge, has not been previously documented in the literature. Additionally, this research represents the first instance of employing ML techniques to generate data for addressing this specific type of problem, further advancing the field of die-sinking EDM. The pivotal machining parameters include discharge current, pulse time and machining depth. Three ML approaches are implemented in this investigation: Artificial Neural Network (ANN), Adaptive-Network-Based Fuzzy Inference System (ANFIS), and Support Vector Machine (SVM). In comparison with experimental outcomes, the ANN technique exhibited superior predictive ability with an coefficient of determination ( R ² ) of 0.99985 and an Mean Relative Error (MRE) of 0.854%. Four distinct EDM machining scenarios are presented and machining parameters and protrusion dimensions are optimized using the ANN technique to decrease tool shape degeneration. Optimizing the machining parameters and diagonal dimensions of the protrusion substantially reduced tool shape degeneration. This research demonstrates the effectiveness of ANN in optimizing machining parameters and improving tool performance in die-sinking EDM. A significant reduction in total wear area of 66.7% was achieved with a considerably lower time cost through the optimized ANN network. While the study demonstrates promising results, its reliance on specific datasets for training may limit the generalizability of the model to broader machining scenarios.

The most important cost element of electric discharge machining (EDM) is the production of tool electrode (shortly electrode). In the EDM process, copper and its alloys are often used as electrode materials. The machining with EDM without increasing the costs can be achieved by selecting the proper electrode with low production and material costs as well as high workpiece material removal rate (MRR), low electrode wear rate (EWR), and relative wear (RW = MRR/EWR). In this study, the EDM performance outputs, namely, MRR and RW were experimentally investigated for electrolytic copper, CuCr1Zr (with and without aging treatment) and CuCo2Be alloy electrode materials for varying machining parameters. The performance outputs were affected by the electrode material and the applied aging treatment. The aged CuCr1Zr alloy electrodes had higher electrical conductivity and better machining performance than the as-received alloy. The CuCo2Be alloy electrodes exhibited moderate to high MRR; however, their RW was the highest. Although the electrolytic copper has moderate MRR performance compared to the investigated alloys, its low cost increased its performance index, making it a more suitable electrode material for EDM applications.

The volume of material removed from the workpiece by a pulse (V _v ) in the electric discharge machining was quantitatively determined using a multi-physics simulation model in ANSYS CFX software. Electrical heating is used in the model to simulate the plasma channel formation by defining the boundary and time-controlled current density initial conditions. Time-dependent physical properties at plasma temperature were used to reflect the actual processing environment. The heat was transferred from the plasma channel to the workpiece by electrical heating from the electrode, and V _v was calculated by means of the amount of heat transfer. The calculated V _v values for AISI4140, Ti6Al4V and Inconel 718 workpieces were lower than the experimental results and the difference was observed to change between 38.3% and 46.9%.

En tr In electric discharge drilling (EDD), rapid local heating and cooling of the workpiece surface by means of electric discharges results in surface layers (recast, heat affected and base material). In this study, the main workpiece surface texture measures, namely the recast layer thickness (RLT) and average surface roughness (R_a) characteristics were investigated for varying machining parameters (discharge current and pulse on time). The conducted experiments revealed the strong dependence of surface characteristics on machining parameters. In electric discharge drilling (EDD), rapid local heating and cooling of the workpiece surface by means of electric discharges results in surface layers (recast, heat affected and base material). In this study, the main workpiece surface texture measures, namely the recast layer thickness (RLT) and average surface roughness (R_a ) characteristics were investigated for varying machining parameters (discharge current and pulse on time). The conducted experiments revealed the strong dependence of surface characteristics on machining parameters.

The study determined (i) the effects of electrical discharge machining (EDM) on the shear-bond strength (SBS) of the bond between luting resin and zirconia ceramic and (ii) zirconia ceramic's flexural strength with the three-point bending (TPB) test. Sixty 4.8mm×4.8mm×3.2mm zirconia specimens were fabricated and divided into four groups (n=15): SBG: sandblasted+silane, TSCG: tribochemical silica coated+silane, LTG: Er:YAG laser treated+silane, EDMG: EDM+silane. The specimens were then bonded to a composite block with a dual-cure resin cement and thermal cycled (6000 times) prior to SBS testing. The SBS tests were performed in a universal testing machine. The SBS values were statistically analyzed using ANOVA and Tukey's test. To determine flexural strength, sixty zirconia specimens were prepared and assigned to the same groups (n=15) mentioned earlier. After surface treatment TPB tests were performed in a universal testing machine (ISO 6872). The flexural strength values were statistically analyzed using ANOVA and Tukey's test (α=0.05). The bond strengths for the four test groups (mean±SD; MPa) were as follows: SBG (Control), 12.73±3.41, TSCG, 14.99±3.14, LTG, 7.93±2.07, EDMG, 17.05±2.71. The bond strength of the EDMG was significantly higher than those of the SBG and LTG (p0.05). The EDM process improved the SBS. In addition, there was no significant adverse effect of EDM on the flexural strength of zirconia.

The main objective of this study is to investigate the effect of Cu–Cr and Cu–Mo powder metal (PM) tool electrodes on electrical discharge machining (EDM) performance outputs. The EDM performance measures used in the study are material removal rate (MRR), tool electrode wear rate (EWR), average workpiece surface roughness (R_a), machined workpiece surface hardness, abrasive wear resistance, corrosion resistance, and workpiece alloyed layer depth and composition. The EDM performance of Cu–Cr and Cu–Mo PM electrodes produced at three different mixing ratios (15, 25, and 35 wt% Cr or Mo), compacting pressures (P_c = 600, 700, and 800 MPa), and sintering temperatures (T_s = 800, 850, and 900 °C) are compared with those machined with electrolytic Cu and Cu PM electrodes when machining SAE 1040 steel workpiece. Analyses revealed that tool materials were deposited as a layer over the work surface yielding high surface hardness, strong abrasion, and corrosion resistance. Moreover, the mixing ratio, P_c, and T_s affect the MRR, EWR, and R_a values.