PSI - Issue 10

E.L. Papazoglou et al. / Procedia Structural Integrity 10 (2018) 235–242 E.L. Papazoglou et al. / Structural Integrity Procedia 00 (2018) 000 – 000

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3.1. Material removal ratio

Increasing the mean machining power results to higher MRR, with the between them correlation being almost linear, as it can be seen in Fig. 3. On the other hand, increasing the nominal pulse discharge energy does not mean necessarily a higher MRR, which strongly depends on the combination of machining parameters. It may be said that for the same amount of energy per pulse, the machining current increase and the proportional reduction of pulse-on time results to higher MRR. But MRR has a limitation on its increase by increasing only the current and keeping the pulse-on time constant, see Fig. 4. Gostimirovic et al. (2012) through their study have come to the conclusion that the increase of discharge current is limited by the current density, with a critical value of 15 A/cm 2 ; higher current density leads to the decrease of MRR. Their explanation is that in real machining condition there are two major factors affecting the efficiency of machining: the presence of debris in the discharge zone and the gas bubble formation. Due to imperfect evacuation of machining products, a portion of discharge energy is spent on re-melting and evaporation. At the same time, a portion of discharge energy takes place in a gaseous environment and thus is irreversibly lost. As a result of the above mentioned and the experimental results of the research, there is an optimum combination of machining parameters for the highest process efficiency and the highest MRR, having always in mind other machining requirements such as the workpiece surface quality and dimensional accuracy.

Fig. 3. MRR vs. mean machining power

Fig. 4. MRR vs. nominal discharge energy per pulse

3.2. Surface roughness

Surface roughness and the dull isotropic appearance of the EDMed surface is the result of overlapping craters, which are formed by the action of the spark discharges. Of the many parameters available to quantify the surface roughness, two of the most commonly used in EDM are the arithmetical mean Ra and the maximum peak-to-valley Rt. The discharge energy is related with the crater geometry, i.e. depth and diameter, and, therefore, with the surface roughness, with the relation between SR, pulse-on time and pulse current being a subject of scientific inquiry. Cavaleri et al. (2017) used the artificial neural network method to predict the mean surface roughness of EDMed surfaces. According to Mascaraque - Ramirez and Franco (2015) roughness parameters present a good adjustment to a linear relation as a function of pulse current, with worse surface finish for higher values of pulse current. Gostimirovic et al. (2012) and Shabgard et al. (2011) correlate SR with the discharge energy and the intensity of the spark. Petro poulos et al. (2006) developed a multi-parameter surface texture model for EDMed surfaces of AISI D2 tool steel. There is not any totally accurate analytical model to correlate Ra and Rt with the machining parameters. Never theless, there are many semi-empirical equations based on experimental and/or numerical approaches. From the experimental data and making the assumption of a nonlinear relation, Eqs. (5) and (6) emerged: 0.3158 0.0848 2.3355 P on Ra I T    (5)

(6)

0.2426 0.0495 24.1033 P on I T  

Rt



with Ra and Rt in μ m, I P in A and T on in μ s. The form of the equations is in accordance with the results of other researchers, having the expected differences in the values of the empirical constants. Keeping in mind the stochastic nature of the process, the simplicity of the