Abstract: Electrical discharge machining (EDM) is a non-traditional process that uses the electrical spark discharge to machine electrically conducting materials for geometrically complex shapes or hard materials. In the current work, cupper and brass were used as the electrode material, and AISI H13 steel as the workpiece. Different input parameters were investigated namely: 20, 30, 40 A current, 50, 100, 150 μs pulse on time, and 1, 3, 6 mm gap. The workpiece thickness was fixed to 4 mm and the pulse off time was 25 μs. All EDM experiments were carried out in diesel oil and the voltage was 140 V. The results showed that the electrode wear decreased with increasing the pulse on time and gap and increased with increasing current for copper and brass electrode. The optimal conditions for minimum tool wear were: pulse on time 150 μs, current 20 A, and gap 6 mm for copper and brass electrodes. Electrode wear is minimum for copper at all parameter values compared to brass electrode.
Electrical discharge machining (EDM) is one of the earliest non-conventional machining in order to manufacture very accurate 3-D complex components on any electrically conductive materials. In die-sinking EDM, a pulse discharge occurs in a small spark gap between electrically conductive workpiece and electrode in dielectric medium. This paper proposed a new integrated control system using Programmable System-on-Chip (PSoC) for Die-sinking EDM in order to enhance Material Removal Rate (MRR). The MRR result of EDM-PSoC system is higher than EDM-Ben Fleming system due to the effect off high speed processing data analysis using PID algorithm in PSoC microcontroller and leads to improving system efficiency 41%.
This paper studies prediction the values of MRR and surface roughness in Electrical discharge operations. It is a operation in which the material removal rate is machined with elevation spark in the midst work piece and electrode sunken through dielectric solution.Through use Taguchi found that the accuracy of the measured and prediction values that have been is 93% and 99% for each of the MRR and surface roughness respectively. The effect of different Electrical discharge machining factors are (Gap, pulse off time and pulse on time) to predict the (material removal rate) and (roughness). Note that connected pole that was used is copper. From (ANOVA) found that the large parameter effect on MRR is pulse-on 65% and pulse-off 25% while large parameter effect for surface roughness is pulse-on 96% . The least influential parameter for metal removal rate is the gap and the least influential parameter for surface roughness is pulse-off and Gap.
In this work an investigation of the effects of various process parameters of Wire-EDM like Servo Feed (SF), pulse off-time (T OFF ), pulse on-time (T ON ), as inputs impact on surface roughness (Ra) and metal removal rate (MRR) as outputs on steel (AISI 1015) utilizing nine specimens. With servo feed (500, 600 and 700)mm/min, pulse-of time (10,30,50) sec, pulse on-time (20,25,30) sec. The characteristics of cutting variables were determined by implementing Taguchi experimental design method. The importance level of the cutting variables for metal removal rate and surface roughness is determined by implementing the analysis of variance (ANOVA).
Electro-chemical Machining is significant process to remove metal with using anodic dissolution. Electro-chemical machining use to removed metal workpiece from (7025) aluminum alloy using Potassium chloride (KCl) solution .The tool used was made from copper. In this present the optimize processes input parameter use are( current, gap and electrolyte concentration) and surface roughness (Ra) as output .The experiments on electro-chemical machining with use current (30, 50, 70)A, gap (1.00, 1.25, 1.50) mm and electrolyte concentration (100, 200, 300) (g/L). The method (ANOVA) was used to limited the large influence factors affected on surface roughness and found the current was the large influence factors with (72.17%) . The results of the optimization of comparison of experimental and prediction conditions current at level-1(30 A) , gap at level-1 (1.00mm ) and electrolyte concentration at level-1(100(g/L)) shown the average experiments and prediction surface roughness (1.352) and (1.399) respectively..
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