An Experimental Investigation on Machining of 90W-7Ni-3Fe Alloys Using Wire Electrical Discharge Machining | ||||
| Benha Journal of Applied Sciences | ||||
| Article 79, Volume 8, Issue 5, May 2023, Page 277-284 PDF (457.11 K) | ||||
| Document Type: Original Research Papers | ||||
| DOI: 10.21608/bjas.2023.205396.1156 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Bassem A. Mohamed   1; Saleh Kaytbay2
| ||||
| 1Mechanical Engineering Department Benha Faculty of Engineering, Benha University, Egypt. | ||||
| 2Mechanical Engineering Department Benha Faculty of Engineering, Benha University, Egypt | ||||
| Abstract | ||||
| Tungsten heavy alloys (WHAs) are desired in many applications, including gyroscope rotors for aerospace and spacecraft, die-casting molds, and shielding elements for radiation protection. Owing to their superior physical, chemical, and mechanical characteristics, which as high density, great radiation shielding capability, high strength, low thermal expansion, high anti-impact toughness, and good corrosion resistance. However, such applications require highly precise complex geometries. Cutting WHAs using conventional machining processes is challenging due to processes of WHAs can present problems, obtaining high-precision parts, such as the challenge of getting high accuracy and tight tolerances. Due to WHAs high toughness, tools wear out quickly and break. For manufacturers, this means higher expenses and more work. Additionally, traditional machining methods' high heat generation causes thermal distortion of the machined component and can reduce the life of the cutting tool. In this paper, Wire Electrical discharge machining (WEDM) is utilized for investigating the machinability of WHAs with a composition of 90W7Ni3Fe. Half factorial design of the experiment was conducted to investigate the effect of machining variables on the material removal rate. The variables include arc-on time (AON), arc-off time (AOF), open voltage (OV), feed rate (FR), servo voltage (SV), and wire tension (WT). An additional ANOVA test was conducted to investigate the significant effect of each variable. The results showed that (FR) has the highest impact on MRR, followed by (SV), (OV) and (AON) which in turn enhance the surface quality. A regression model was developed for predicting the MRR with an accuracy of 90.37%. | ||||
| Keywords | ||||
| 90W7Ni3Fe; WHAs; WEDM; MRR; Half factorial | ||||
|
Statistics Article View: 232 PDF Download: 202 |
||||
