A hybrid Grey-TOPSIS based quantum behaved particle swarm optimization for selection of electrode material to machine Ti6Al4V by electro-discharge machining

Keyphrases

• Ti-6Al-4V (Ti64) 100%
• Electrode Materials 100%
• Electrical Discharge Machining 100%
• Quantum Particle Swarm Optimization 100%
• Grey TOPSIS 100%
• Material Removal Rate 66%
• Tool Electrode 66%
• Machined Surface 33%
• Scanning Electron Microscopy 33%
• Surface Roughness 33%
• Material Behavior 33%
• Graphite 33%
• Difficult-to-cut Materials 33%
• Energy Dispersive X-ray Spectroscopy 33%
• Spectroscopic Techniques 33%
• AlSi10Mg 33%
• Microhardness 33%
• Sintering Process 33%
• Multi-objective Optimization 33%
• White Layer Thickness 33%
• Tool Wear Rate 33%
• Engineering Challenges 33%
• Electron Energy 33%
• Post-machining 33%
• Laser Sintering 33%
• Graphite Electrode 33%
• Composite Tools 33%
• Hybrid Intelligent Algorithm 33%
• Surface Crack Density 33%
• Output Response 33%
• Roughness Surface 33%
• Taguchi L27 33%

Engineering

• Particle Swarm Optimization 100%
• Electrodischarge Machining 100%
• Material Removal Rate 66%
• Energy Engineering 33%
• Machined Surface 33%
• Material Behavior 33%
• Design of Experiments 33%
• Engineering 33%
• Layer Thickness 33%
• Cut Material 33%
• Microhardness 33%
• Multiobjective Optimization 33%
• Surface Crack 33%
• Output Data 33%
• Laser Sintering Process 33%
• Graphite Electrode 33%
• Additional Data 33%
• Crack Density 33%
• Output Response 33%