Optimization of wire electrical discharge machining using statistical methods coupled with artificial intelligence techniques and soft computing

El-Bahloul, Sara A.

doi:10.1007/s42452-019-1849-6

Cited by 14 publications

(12 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The WS 2 microparticles appear in the form of a blue-grey powder. The powder consists of Tungsten (74.13%) and Sulphur (25.87%) having a density of 7.5 g/cm 3 ; melting point 1250 °C; particle size 4-5 μm.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of process parameters during WEDM of EN-42 spring steel

Prasad

Upreti²,

Yadav

et al. 2020

SN Appl. Sci.

View full text Add to dashboard Cite

The present work aimed to carry out the machining of EN-42 spring steel in wire electrical discharge machining with zinc-coated brass wire using distilled water as a dielectric medium. The effect of WS 2 powder added in a dielectric medium, on the metal removal rate and surface roughness has been evaluated. With the use of WS 2 , the conductivity of the dielectric is found to be constantly equal to 10 S/m, which is a very promising feature. WS 2 enhances dielectric life, metal removal rate, and surface roughness significantly. The effect of cryogenically treated wire on the metal removal rate and surface roughness during machining has been carried out. Taguchi method and genetic algorithm were used to optimize machining performance measures. Both methods provide optimum parameters and target value. The results of the Taguchi method show more error as compared to the genetic algorithm. The on time can be predicted as the most significant parameter according to the Taguchi method. From the results, it is observed that higher on time shows better metal removal rate and lower on time shows a better surface finish. Keywords Cryogenic treatment • Metal removal rate • Root mean square surface roughness • Taguchi • S-method • Genetic algorithm • EN-42 spring steel Abbreviations EDM Electrical discharge machining TWR Tool wear rate ASTM American society for testing and materials S/N Signal to noise ratio WEDM Wire electrical discharge machining Notations T on On time, µS T off Off time, µS F wf Wired feed, M/min F sf Servo feed, M/min MRR Material removal rate, m 3 /min

show abstract

Section: Methodsmentioning

confidence: 99%

“…The electric discharge or spark erosion for cutting takes place under the blanket of dielectrics such as mineral oil or kerosene fed to the container in which EDM takes place. The removed particles are getting flushed away with dielectric fluid flow [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Optimization of process parameters during WEDM of EN-42 spring steel

Prasad

Upreti²,

Yadav

et al. 2020

SN Appl. Sci.

View full text Add to dashboard Cite

show abstract

“…Optimization of machining process parameters is also an essential and challenging task to obtain the best possible machinability, specially when machine DTM materials using advanced machining processes [15][16][17]. As regards to that review of some past literature on optimization of EDM and wire-EDM parameters for DTM materials, using hybrid intelligent techniques, has been done [18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

“…The optimum values of responses were 4.0125 mm 3 /min MRR, 0.00012 gm/min tool wear rate, and 2.28 µm surface roughness. El-Bahloul [22] conducted research work on optimization of wire-EDM parameters using statistical method integrated with Fuzzy and successfully improved WEDM productivity and surface quality of 304 stainless steel.…”

Section: Introductionmentioning

confidence: 99%

Intelligent optimization of wire-EDM parameters for surface roughness and material removal rate while machining WC-Co composite

Gupta¹

2021

FME Transactions

View full text Add to dashboard Cite

This work presents the wire-EDM of WC-Co composite and optimization of process parameters using an integrated technique of response surface methodology (RSM), Vise Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) and artificial bee colony (ABC) algorithm to obtain the best set of machinability indicators. Wire feed (WF), servo voltage (SV), pulse off-time (Pon) and pulse on-time (Poff) are the variable process parameters, whereas root mean square roughness (Rq), average surface roughness (Ra) and material removal rate (MRR) are the machinability indicators considered in the present work. A total of twenty nine experiments have been conducted based on Box Behnken design (BBD) technique of response surface methodology. VIKOR has been used for normalization of responses and followed by solving empirical models using ABC algorithm to obtain optimized process parameters setting. WF-12 m/min, SV-65V, Pon-116 µs, Poff-20 µs are the optimum wire-EDM parameters obtained by intelligent RSM-VIKOR-ABC technique that produced best values of Ra-4.51 µm, Rq-5.64 µm, MRR-0.061 mm3 /min simultaneously. The validation test confirmed an improvement up to 15% in the response characteristics which proved the effectiveness of this novel hybrid technique for optimization. The optimum parameter setting is for ready industrial reference to attain best surface quality and process productivity for WC-Co composite machining by wire-EDM.

show abstract

“…Mean roughness depth of 6.2 µm and 0.021 g/min material removal rate have been achieved after machining at optimum parameters produced by hybrid optimization technique. In an another important study, El-Bahloul [15] successfully improved process performance and stainless steel 304 part quality using response surface technique integrated Fuzzy approach. The research work reported by Tzeng et al [16] highlights the effectiveness of back-propagation neural network (BPNN), a genetic algorithm (GA), and response surface methodology (RSM) based hybrid technique for the WEDM parameter optimization to obtain better material removal rate (0.2704 g/ min) and average surface roughness (1.3561 µm) when machining tungsten carbide.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Machining of Shape Memory Alloys

Gupta¹

2021

Adv. Sci. Technol. Res. J.

View full text Add to dashboard Cite

Shape memory alloys are important biomaterials but difficult-to-machine (DTM). Their machining needs to be done using intelligent techniques to obtain a better machinability. Hybrid optimization is one of such techniques which can perform modeling and optimization of machining parameters for the best values of machinability indicators. Wire electric discharge machining (WEDM) of shape memory alloy has been found as a prominent alternate to the conventional machining techniques; however it needs the assistance of intelligent techniques to machine such materials to obtain the optimum values of machinability indicators. In this paper, WEDM of shape memory alloy Ni 55.8 Ti was reported. WEDM was carried out by varying four process parameters i.e. servo voltage SV, pulse-on time P on , pulse-off time P off , and wire feed rate WF using Taguchi L 16 robust design of experiment technique. A hybrid optimization technique TOPSIS-Fuzzy-PSO has been successfully used to optimize these parameters (SV-50V; P on -1µs; P off -17 µs; WF-4 m/min) for the best possible values of material removal rate (MRR) -0.049 g/min, maximum roughness -11.45 µm, and recast layer -22.10 µm simultaneously.

show abstract

Optimization of wire electrical discharge machining using statistical methods coupled with artificial intelligence techniques and soft computing

Cited by 14 publications

References 9 publications

Optimization of process parameters during WEDM of EN-42 spring steel

Optimization of process parameters during WEDM of EN-42 spring steel

Intelligent optimization of wire-EDM parameters for surface roughness and material removal rate while machining WC-Co composite

Intelligent Machining of Shape Memory Alloys

Contact Info

Product

Resources

About