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Effect of Pulse on Time and Pulse off Time on Material Removal Rate and Electrode Wear Ratio of Stainless Steel AISI 316L in EDM | ||
| Engineering and Technology Journal | ||
| Article 1, Volume 34, 15A, 2016, Pages 2940-2949 PDF (750.83 K) | ||
| DOI: 10.30684/etj.34.15A.14 | ||
| Authors | ||
| Shukry H. Aghdeab; Amani I. Ahmed | ||
| Abstract | ||
| The electrical discharge machining (EDM) is one of the non-traditional cutting processes, used in many important applications such as dies and auto industry. This thesis focuses on the study of machining responses such as material removal rate (MRR) and electrode wear ratio (EWR) under the effect of different machining conditions in EDM process. The process parameters are pulse on time (Ton), pulse off time (Toff) and electrical current (Ip). The main purpose of this work is to achieve best MRR and least EWR using copper electrode with fixed diameter (10 mm) for the machining of stainless steel AISI 316L with a constant thickness (0.8 mm). Different values for the Ton (25, 50 and 75) µs, Toff (9, 18 and 25) µs and Ip (16, 30 and 50) A were used. The results of experiments showed the main effects of machining conditions on MRR and EWR. Where, the MRR increased with increasing the Ton, MRR decreased with increasing the Toff and MRR increased with increasing Ip. While, the EWR decreased with increasing the Ton, EWR decreased with increasing Toff until access to a specific Toff then EWR increased with longer Toff and EWR increased with increasing Ip. The maximum MRR is (48.16 mm3/min) at Ton (75 µs), Toff (9 µs) and Ip (50 A) and minimum EWR is (0.179 %) at Ton (75 µs), Toff (9 µs) and Ip (16 A). The results showed that the response surface methodology (RSM) that have been performed using Minitab 17 software. It can predict the machining responses with a good accuracy where it gave the coefficient of determination (R-sq) that determines the degree of fit between the experimental and predicted data. Higher value of R-sq shows a better fit. The values of R-sq are (97.46 %) for the MRR and (96.34 %) for EWR. Also, the SPSS 18 software was used to predict the machining responses with a good accuracy. | ||
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