Machinability analysis in wire-EDM of cryogenically treated Ti6Al4V alloy and multi-objective optimization using...

Area of Science:

• Materials Science
• Manufacturing Engineering
• Surface Engineering

Background:

• Ti6Al4V alloy's poor wear resistance limits its use in demanding applications.
• Conventional machining of Ti6Al4V is challenging due to its properties, impacting biomedical implant longevity.
• Cryogenic treatment offers a potential solution to enhance wear resistance.

Purpose of the Study:

• To investigate the machinability of cryogenically treated and untreated Ti6Al4V using wire electrical discharge machining.
• To model and optimize material removal rate (MRR) and surface roughness (Ra).
• To compare the effectiveness of genetic algorithm (MOGA) and African vultures optimization algorithm (MOAVOA) for multi-objective optimization.

Main Methods:

• Wire electrical discharge machining (WEDM) of Ti6Al4V.
• Rotary central composite design and response surface methodology for modeling.
• Multi-objective optimization using MOGA and MOAVOA.

Main Results:

• Discharge current significantly influenced MRR in cryogenically treated samples (58.04%).
• Duty cycle and discharge current were dominant linear terms for surface roughness, while current dominated the square term.
• MOAVOA demonstrated superior performance over MOGA in optimization, providing better convergence and solution diversity.

Conclusions:

• Cryogenic treatment enhances Ti6Al4V machinability via WEDM.
• Optimized WEDM parameters can improve both material removal and surface finish.
• MOAVOA is a highly effective algorithm for optimizing complex machining processes.