Process Parameter Optimization of Wire EDM on Aluminum and Mild Steel by Using Taguchi Method

Tilekar, Shivkant D.; Das, Sankha Shuvra; Patowari, Promod Kumar

doi:10.1016/j.mspro.2014.07.518

Cited by 38 publications

(22 citation statements)

References 7 publications

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“…EN31 Tool Steel is chosen as the material for the study and optimization due to three primary reasons-(1) Not much research has been done for the study of EN31 Tool Steel in wire EDM process. (2) It is one of the most versatile tool steel used for various purposes. (3) Due to its poor machinability.…”

Section: Methodsmentioning

confidence: 99%

“…MRR is beneficial type as the higher value of MRR is needed; hence, it is normalized using Eq. (2). The mean values of the normalized data of the attribute Surface Roughness (R a ) and Material Removal Rate (MRR) are computed using Eq.…”

Section: Preference Selection Indexmentioning

confidence: 99%

“…In past response surface methodology has been used to enumerate the relationship between input and output responses in the WEDM process and the effects of input process parameters on MRR and surface roughness were studied by Gowd et al [1]. Tilekar et al [2] investigated to analyze the effect of process parameters on surface roughness and kerf width of aluminium and mild steel. Taguchi method opted for the optimization.…”

Section: Introductionmentioning

confidence: 99%

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PSI and TOPSIS Based Selection of Process Parameters in WEDM

Diyaley

Shilal²,

Shivakoti

et al. 2017

Period. Polytech. Mech. Eng.

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IntroductionWith the development of technology, more challenging problems are being faced by the technologists in the field of manufacturing. The difficulty in adopting the conventional manufacturing processes can be attributed mainly to the machining principle that requires a cutting tool which must be harder than the material of the work-piece. New materials which have a high strength-to-weight ratio, heat resistance and high hardness are tough to machine and also timeconsuming by conventional methods. Hence, advanced machining processes are being developed to overcome these difficulties. These newly developed techniques provide a very high accuracy and can machine complexly shaped surfaces. These processes, however, are not meant for replacing the conventional processes but are a supplement to them.Optimization and selection of proper process parameters play a vital role in any of the manufacturing or metal processing industries. Selection of proper cutting conditions is essential for efficient machining. Improper selection of machining parameters leads to several adverse effects during machining. Optimization technique provides a proper selection of cutting parameters. Many research attempts have been made to select the proper cutting parameters in WEDM. In past response surface methodology has been used to enumerate the relationship between input and output responses in the WEDM process and the effects of input process parameters on MRR and surface roughness were studied by Gowd et al. [1]. Tilekar et al. [2] investigated to analyze the effect of process parameters on surface roughness and kerf width of aluminium and mild steel. Taguchi method opted for the optimization. Spark on time, spark off time, input current and wire feed rate were used as input parameters and ANOVA results showed that spark on time and input current have statistically significant effect in the case of aluminium and mild steel respectively. Dabade and Karidkar [3] analyzed the machining conditions of material removal rate, surface roughness and kerf width and dimensional deviation during WEDM of Inconel 718 using Taguchi technique. It was observed that pulse on time was the most influencing factor. Patel et al. [4] applied AHP/TOPSIS

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Section: Methodsmentioning

confidence: 99%

Section: Preference Selection Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

PSI and TOPSIS Based Selection of Process Parameters in WEDM

Diyaley

Shilal²,

Shivakoti

et al. 2017

Period. Polytech. Mech. Eng.

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show abstract

“…MRR was found to be greatly influenced by Pon whereas the impact of wire tension was prominent for Ra. In another study, Tilekar [21] developed the optimal settings of WEDM parameters for cutting aluminum and mild steel using single response optimization technique. Peak current and Pon were proved to be the statistically significant control factors controlling Ra of cut specimen.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, an optimization technique has become an essential tool in finding the exact com-bination of process parameters. Many researchers used various optimization techniques in order to optimize the process parameters of WEDM [6,17,21]. In addition to these, Kumar [8] used taguchi method while investigating the effects of cutting parameters on material removal rate for EN-42 using WEDM process.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Effect of Wire Electric Dischage Machining Process Parameters for the Formation of High Speed Steel Form Tool

Ishfaq¹,

Mufti²,

Ahmad³

et al. 2018

Adv. Sci. Technol. Res. J.

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The use of form tools is growing in industry, owing to their inherent advantage of improving productivity. However, the accurate formation of form tool profile is a really tedious task. In this research work, wire electric discharge machining (WEDM) process has been used for the generation of a form tool. Pulse on-time, pulse off-time, servo voltage, wire tension, flushing pressure have been considered as input parameters, whereas tool geometry (clearance angle, included angle), surface roughness and material removal rate are the selected responses. Response surface methodology (RSM) (Box Bhenken experimental design) technique has been used for design of experiments. Analysis of Variance (ANOVA) has shown that pulse on-time and pulse off-time are the two influential control factors for material removal rate (MRR), surface roughness (Ra), clearance angle and included angle. Contour plot analysis has been performed to find out the optimal ranges of the most influential control factors for each response characteristics.

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