Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II

Mandal, Debabrata; Pal, Surjya K.; Saha, Partha

doi:10.1016/j.jmatprotec.2006.12.030

Cited by 230 publications

(115 citation statements)

References 7 publications

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“…al. [25] attempted to model and optimizes the complex electrical discharge machining (EDM) process using soft computing techniques. Artificial neural network (ANN) with back propagation algorithm was used to model the process.…”

Section: Elk Asia Pacific Journals -Special Issue Isbn: 978-81-930411mentioning

confidence: 99%

Modelling of Electro-Discharge Machining of Difficult-to-Machine Materials: An Overview

2015

International Conference on Advancements and Recent Innovations in Mechanical, Production and Industrial Engineering

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Abstract-Electro discharge machining (EDM) is anunconventional electro thermal machining process used for manufacturing geometrically complex or hard material parts that are extremely difficult-tomachine by conventional machining process. Modelling of the EDM process provides a functional relationship between operating parameters and output parameters (process performance index) of EDM process. This is the scientific way to study quantitatively EDM process behaviour which helps to get the better understanding of the process. This paper reviews various research work carried out within past decades for the development of EDM process. The study is mainly focused on aspects related to the experimental modelling of EDM process using Artificial Neural Network (ANN) and Response Surface Methodology (RSM).

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Section: Elk Asia Pacific Journals -Special Issue Isbn: 978-81-930411mentioning

confidence: 99%

Modelling of Electro-Discharge Machining of Difficult-to-Machine Materials: An Overview

2015

International Conference on Advancements and Recent Innovations in Mechanical, Production and Industrial Engineering

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“…Tzeng and Chen (2007) applied fuzzy logic analysis coupled with Taguchi's method to optimize the precision and accuracy of the high speed EDM process. Mandal et al (2007) studied the influence of current, pulse on time and pulse off time on MRR and tool wear using multi-objective optimization model. Dhar et al (2007) studied the effect of current, pulse on time and air gap voltage on MRR, TWR and ROC in electric discharge machining of Al-4Cu-6Si alloy-10 wt% SiCp composites.…”

Section: Introductionmentioning

confidence: 99%

“…It is observed from the study of past research works that there are many multiresponse optimization techniques being used by the researchers, out of which the multiresponse signal to noise (MRSN) ratio method, weighted signal to noise (WSN) ratio method (Shiau, 1990;Tai et al, 1992), grey relational analysis (GRA) method (Singh et al, 2004), and VIKOR method (Tong et al, 2007;Nayak and Rana, 2016) are quite simpler as compared to other techniques (Chiang et al, 2007;Mandal et al, 2007;Sengottuvel et al, 2013;Teimouri and Baseri, 2014;Das et al, 2014). Those optimization techniques make use of complex statistical procedures and softwares, which may be inconvenient to be used by the industrial personnel having little knowledge of software and statistics.…”

Section: Introductionmentioning

confidence: 99%

Performance optimization in electro- discharge machining using a suitable multiresponse optimization technique

Nayak¹,

Rana²,

Parida³

2017

10.5267/j.dsl

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In the present research work, four different multi response optimization techniques, viz. multiple response signal-to-noise (MRSN) ratio, weighted signal-to-noise (WSN) ratio, Grey relational analysis (GRA) and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian) methods have been used to optimize the electro-discharge machining (EDM) performance characteristics such as material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR) simultaneously. Experiments have been planned on a D2 steel specimen based on L9 orthogonal array. Experimental results are analyzed using the standard procedure. The optimum level combinations of input process parameters such as voltage, current, pulse-on-time and pulse-off-time, and percentage contributions of each process parameter using ANOVA technique have been determined. Different correlations have been developed between the various input process parameters and output performance characteristics. Finally, the optimum performances of these four methods are compared and the results show that WSN ratio method is the best multiresponse optimization technique for this process. From the analysis, it is also found that the current has the maximum effect on the overall performance of EDM operation as compared to other process parameters.Growing Science Ltd. All rights reserved. 7

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“…Mandal, et al [7] modeled EDM process using back propagation NN and multi-objective optimization using NDGA-II. Tzeng and Chen [8] have applied fuzzy logic analysis coupled with Taguchi method to optimize the precision and accuracy of the high-speed electrical discharge machining (EDM) process.…”

Section: Introductionmentioning

confidence: 99%

Parametric optimization of wire electrical discharge machining using TOPSIS method

Gadakh¹

2012

Adv produc engineer manag

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Abstract:In this paper, application of techniques for order preference by similarity to ideal solution (TOPSIS) method is applied for solving multiple criteria (objective) optimization problem in wire electrical discharge machining (WEDM) process. Three examples are included to illustrate the approach. In all the cases, it is found that, the results obtained using the TOPSIS method almost match with those derived by the past researchers which prove the applicability of this method while solving various complex decision-making problems in present day manufacturing environment.

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Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II

Cited by 230 publications

References 7 publications

Modelling of Electro-Discharge Machining of Difficult-to-Machine Materials: An Overview

Modelling of Electro-Discharge Machining of Difficult-to-Machine Materials: An Overview

Performance optimization in electro- discharge machining using a suitable multiresponse optimization technique

Parametric optimization of wire electrical discharge machining using TOPSIS method

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