Estimation of Machining Performances Using MRA, GMDH and Artificial Neural Network in Wire EDM of EN-31

Ugrasen, G.; Ravindra, H.V.; Prakash, G. Vijaya; Keshavamurthy, R.

doi:10.1016/j.mspro.2014.07.209

Cited by 20 publications

(5 citation statements)

References 12 publications

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“…The Taguchi's method of creating the DOE is mostly preferred due to the fact that the number of experiments is the least in this method [10]. In the literatures [11][12][13][14], the authors showed that peak current (I.P. ), pulse on time (T on ), pulse off time (T off ), servo voltage (SV) and percentage of B 4 C (% B 4 C) significantly effects the material removal rate (MRR), surface roughness R a and R z and kerf thickness ( ) while machining Al5083/B 4 C composite by WEDM.…”

Section: Literature Reviewmentioning

confidence: 99%

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Modelling and optimizing performance parameters in the wire-electro discharge machining of Al5083/B4C composite by multi-objective response surface methodology

Singh

Hussain

Dash

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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Composite is an artificial multiphase material that constituent phases which are chemically dissimilar and separated by a distinct interface. As a result of such anisotropic and non-homogeneous formation machining of composites is a strenuous task. Therefore, the need arose for formulating a stable predictive model. The comprehensive intention of this study is to develop a highly robust and stable predictive soft-computing model for forecasting the machining performance of Al-5083 alloy reinforced with B 4 C particles (Al5083/B 4 C). The study mostly emphasize on selecting the best machining parameters among the conducted experiments and evaluating the optimal machining parameters for Al5083/B 4 C composite in wireelectro discharge machin. The paper is an integration of equally weighted experimental as well as computational study. In the experimental part of the study, 5 different specimen of Al5083/B 4 C is prepared by the ex situ technique through stir casting process. The experimental part includes design of experiment by Taguchi's method. The computational part of the study comprised of three different stages. The first stage involves the mathematical modelling of the performance measures and statistical scrutiny of the models. In the second stage, the best machining parameters are selected based on the fuzzy IFthen rules. The final stage of the manuscript is the trade-off analysis conducted to obtain the optimal machining parameters. In order to test the robustness of the formulated model an experimental validation is carried out at the optimal machining combination. The error calculated from the comparison is within the range of 2-5% which justifies the objective of the study.

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Section: Literature Reviewmentioning

confidence: 99%

“…Artificial Neural Network (ANN) is a better and developed way of modelling the performance measures. In [12], ANN is used to develop a mathematical model for predicting MRR and surface roughness. In [15], Elman-Based layer recurrent network for accurately predicting the performance measures of WEDM.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling and optimizing performance parameters in the wire-electro discharge machining of Al5083/B4C composite by multi-objective response surface methodology

Singh

Hussain

Dash

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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“…It was observed that pulse-on time and peak current were the most significant parameters. Ugrasen et al (2014) developed a multi regression model and group method data handling technique for wire electric discharge machining process and estimated the best fit of data for SR and MRR. Kumar et al (2015) proposed a combined model Taguchi’s method and gray relational analysis (GRA) to optimize process parameters of WEDM for machining of Aluminium 6351.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of wire electrical discharge machining of 20MnCr5 alloy steel

Kumar

Goyal

2020

WJE

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Purpose Wire electric discharge machining (WEDM) is a non-conventional machining process, which is used to provide difficult and intricate shapes. The purpose of this research work is to apply Taguchi’s technique to optimize the process parameters in WEDM. Alloy steel 20MnCr5 has been selected as base material for experimentation. The effects of the input process parameters such as wire type, pulse-on time, pulse-off time, peak current, wire feed rate and servo voltage have been calculated on the material removal rate (MRR) and surface roughness (Ra) in WEDM operation. Design/methodology/approach In the research work, Taguchi's technique is applied to optimize the process parameters in WEDM. Findings ANOVA indicated that pulse-off time was the most significant factor for the MRR, and servo voltage was the most significant factor for surface roughness (SR). As a part of the project, 20MnCr5 was machined in wire electric discharge machine, and the optimal control parameters were found to get higher MRR and better SR using Taguchi’s technique. Originality/value To the best of authors’ knowledge, after reviewing the literature, materials including alloys of metals such as 16MnCr5 and 20MnCr5 have not been investigated so far, and research regarding machining of these materials is limited. Therefore, 20MnCr5 material has been selected for this research work to generate WEDM data.

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“…Mohanty and Nayak [25] has applied Taguchi method to optimize the MRR and SR as response parameters. Ugrasen et al [26] developed a model to estimate the optimal machining performances of WEDM process using multiple regression analysis (MRA), group method data handling technique (GMDH) and ANN in machining of EN31 so as to obtain the optimal responses of accuracy, surface roughness and volumetric material removal rate. Diyaley et al [27] has applied the combination of preference selection index (PSI) and TOPSIS to obtain the parametric combination of WEDM process while machining of EN31 steel.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Wire Electro Discharge Machining (WEDM) Process Parameters Using Grey-Fuzzy Approach

Das

Diyaley

Chakraborty

et al. 2018

Period. Polytech. Mech. Eng.

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Wire electro discharge machining (WEDM) is a versatile non-traditional machining process that is extensively in use to machine the components having intricate profiles and shapes. In WEDM, it is very important to select the optimal process parameters so as to enhance the machine performance. This paper emphasizes the selection of optimal parametric combination of WEDM process while machining on EN31 steel, using grey-fuzzy logic technique. Process parameters such as servo voltage, wire tension, pulse-on-time and pulse-off-time were considered while taking into account several multi-responses such as material removal rate (MRR) and surface roughness (SR). It was found that pulse-on-time of 115 µs, pulse-off-time of 35 µs, servo voltage of 40 V and wire tension of 5 kgf results in a larger value of grey fuzzy reasoning grade (GFRG) which tends to maximize MRR and improve SR. Finally, analysis of variance (ANOVA) is applied to check the influence of each process parameters in the estimation of GFRG.

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Estimation of Machining Performances Using MRA, GMDH and Artificial Neural Network in Wire EDM of EN-31

Cited by 20 publications

References 12 publications

Modelling and optimizing performance parameters in the wire-electro discharge machining of Al5083/B4C composite by multi-objective response surface methodology

Modelling and optimizing performance parameters in the wire-electro discharge machining of Al5083/B4C composite by multi-objective response surface methodology

Multi-objective optimization of wire electrical discharge machining of 20MnCr5 alloy steel

Multi-Objective Optimization of Wire Electro Discharge Machining (WEDM) Process Parameters Using Grey-Fuzzy Approach

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