Wire Electrical Discharge Machining Process: Challenges and Future Prospects | ||
| International Journal of Materials Technology and Innovation | ||
| Article 5, Volume 2, Issue 2, December 2022, Pages 31-37 PDF (600.22 K) | ||
| Document Type: Review Article | ||
| DOI: 10.21608/ijmti.2022.165776.1061 | ||
| Authors | ||
| Mostafa Shehata* 1; Shimaa El-Hadad2; Helmi Attia3 | ||
| 1Central Metallurgical Research & Development Institute | ||
| 2Central Metallurgical R&D Institute | ||
| 3McGill University, Montreal, Quebec, Canada | ||
| Abstract | ||
| Titanium alloys, due to their distinctive properties, are used in a wide range of modern applications. However, because these alloys are challenging to machine using traditional methods, nonconventional processes are more often utilized. One of the unique thermal machining techniques that offers an effective choice for creating components with the highest degree of dimensional accuracy and surface finish quality from difficult-to-machine materials such as titanium alloys is Wire Electrical Discharge Machining (WEDM). . In the first part of this paper try to highlight the research trends in WEDM on finding the relationships among various process parameters, such as pulse on time, pulse off time, servo voltage, peak current, dielectric flow rate, cutting speed, wire tension, and machining modes, which have a crucial impact on a variety of process responses, such as material removal rate (MRR), surface roughness (Ra), sparking gap (Kerf width), and wire wear ration (WWR), as well as surface integrity. The second part of article also discusses various modeling, simulation, and optimization technique for monitoring process parameters to investigate the feasibility of various control practices for achieving the best machining conditions. The final part of the paper discusses these developments and includes some recommendations about the possible trends for future WEDM researches. | ||
| Keywords | ||
| Titanium alloy; Wire electrical discharge machining (WEDM); Process optimization parameters; modeling and simulation | ||
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