Gorilla Troops Optimizer Combined with ANFIS for Wire Cut EDM of Aluminum Alloy

Table 1

Significant published articles on WEDM in recent years.

Reference	Material used	Contribution
[7]	Al6061/SiC/graphite	Brass wire, zinc coated wire, and diffused coated wires were investigated based on the speed. They reported that diffused coated wire is the best of these wire materials.
[8]	Carbon steel 1017 and aluminum alloy 6060	Wire feed rate (3, 5 and 7 mm/minute) was investigated and it was reported that low feed rate is the best to improve the surface finish.
[9]	High speed steel (HSS) M2 grade	Material removal rate, surface roughness, and width of kerf were analyzed using GRA. They used tool makers’ microscope to measure the amount of material wasted during machining, known as kerf width.
[10]	Titanium alloy	Cause of wire rupture which subsequently affects the production and productivity was investigated. They reported that stay of debris caused by low flushing pressure and high wire tension caused by instantaneous high temperature during the machining are two major reasons for wire rupture.
[11]	Friction stir welded 5754 aluminum alloy	RSM was used to optimize the process variables in machining friction stir welded 5754 aluminum alloy.
[12]		Dry cutting, cryogenic cutting, minimum quantity lubrication (MQL), nanocutting fluids, and MQL nanofluids were investigated and it was reported that MQL nanofluid and cryogenic machining showed the best sustainable strategies.
[13]	LM13 aluminum alloy	Slotted copper tool was used to drill aluminum alloy and it was reported that modified tool design has given higher surface finish and material removal rate.
[14]	AISI 304 stainless steel	The influence of various parameters was investigated and it was reported than wire tension influences greatly the surface finish, removal rate, and microhardness of the material. It does not influence the kerf width.
[15]	High carbon high chromium steel	Zirconium powder mixed dielectric fluid was used and it was reported that it has improved the performance of the machining.
[16]	Pure titanium	Multiresponse optimization was conducted using integrated approach of RSM and GRA. They reported the optimal parameters are Ton = 6µs, Toff = 4µs, and discharge current = 6A.
[17]	LM13 Al alloy/10ZrB2/5TiC hybrid composite	Multiresponse optimization was conducted using RSM and GRA. Their aim was to investigate the effect of process parameters on material removal rate (MRR), electrode wear rate (EWR), and overcut (OC). They finally reported that current is the highly influencing parameter on these responses.
[18]	AZ61 magnesium alloy /B4C/SiC hybrid composite	The influencing parameter among percentage of filler reinforcement, stirring speed, time of stirring, and process temperature.
[19]	Al-Si12/B4C/Fly ash hybrid composite	Material removal rate was investigated and it was reported that it highly depends on pulse-on time and fly ash reinforcement.
[20]	AA1050/5 wt.% SiC composite	Zinc coated copper wire was used to machine SiC reinforced aluminum composite and it was reported that open voltage is the significant parameter.
[21]	Monel K-500, a nickel–copper based alloy	WEDM was applied to super alloy machining. Pulse-on time and pulse-off time affect cutting rate proportionally and inverse proportionally, respectively. Spark gap decreases the cutting rate and surface roughness.
[22]	Inconel-800 superalloy	Trapezoidal interval type-2 fuzzy numbers were applied for handling the uncertainties associated with the subjective assessment of the criteria. The integration of T2FS with additive ratio assessment (ARAS) method resulted in the best WEDM process parameters.
[23]	Cemented carbide	This research attempted to compare edges made using WEDM techniques and conventional edges made in the grinding process.
[24]	Ti6Al4V alloy	This work combines integrated fuzzy analytic hierarchy process (AHP) and fuzzy technique for order preference by similarity to ideal situation (TOPSIS) to optimize the WEDM process.
[25]	AZ31 alloy	They used Box-Behnken design (BBD) to conduct experiments and multiobjective particle swarm optimization (MOPSO) to optimize cutting rate and recast layer.