As a successful solution applied to electrical discharge machining (EDM), powder-mixed electrical discharge machining (PMEDM) has been proposed as an upgrade of the EDM process. The optimization of the process parameters of PMEDM is essential and pressing. In this study, Taguchi methods and analysis of variance (ANOVA) were used to find the main parameters affecting surface roughness in the EDM process with SiC powder-mixed-dielectric of hardened 90CrSi steel. The PMEDM parameters selected were the powder concentration, the pulse-on-time, the pulse-off-time, the pulse current, and the server voltage. It was found that SiC powder exhibits positive effects on reducing surface roughness. The roughness obtained with the optimum powder concentration of 4 g/L was reduced by 30.02% compared to that when processed by conventional EDM. Furthermore, the pulse-off-time was found to be the most influential factor that gave an important effect on surface roughness followed by the powder concentration. The EDM condition including a powder concentration of 4 g/L, a pulse-on-time of 6 µs, a pulse-off-time of 21 µs, a pulse current of 8 A, and a server voltage of 4 V resulted in the best surface roughness.

In the current study, an optimization process of powder-mixed electrical discharge machining (PMEDM) process when machining cylindrically shaped parts made of hardened 90CrSi steel is reported. In this study, SiC powder was mixed into the Diel MS 7000 dielectric solution. Additionally, graphite was chosen as the electrode material. The multi-objective functions were minimizing the surface roughness (SR) and electrode wear rate (EWR) and maximizing the material removal rate (MRR). The used input parameters of the optimization process included the powder concentration, the pulse-on time, the pulse-off time, the pulse current, and the servo voltage. A combination between the Taguchi method and the grey relation analysis (GRA) method with the support of Minitab R19 software was used to design the experiment and analyze the results. It was found that the optimal set of process parameters that can satisfy the above responses are Cp of 0.5 g/L, Ton of 8 µs, Toff of 8 µs, IP of 5 A, and SV of 4 V.

This article presents the results of an optimization study on the determination of the best input parameters for the internal grinding process when processing cylindrical-shaped parts of SKD11 tool steel. For this purpose, three Multi-Criteria Decision Making (MCDM) methods including the Evaluation based on Distance from Average Solution (EDAS), Multi-Attributive Border Approximation Area Comparison (MABAC), and Multi Attribute Utility Theory (MAUT) were used to solve the MCDM problem, while the entropy method was applied to find the criteria weights. Three objectives, namely the Surface Roughness (SR), Material Removal Rate (MRR), and wheel life (Tw), were also investigated. Six input factors, involving the coarse dressing depth (ar), coarse dressing times (nr), fine dressing depth (af), fine dressing times (nf), non-feeding dressing (n0), and dressing feed rate (Sd), were examined. Additionally, the Taguchi method with the L16 (44+22) design and the Minitab R19 program were deployed to design this study’s experiment and investigate its outcomes. The MCDM work was successfully solved and the best process factors are proposed.