Analysis of Process Parameters in Wire EDM with Stainless Steel Using Single Objective Taguchi Method and Multi Objective Grey Relational Grade

Durairaj, M.; Sudharsun, D.; Swamynathan, N.

doi:10.1016/j.proeng.2013.09.163

Cited by 162 publications

(60 citation statements)

References 6 publications

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“…Pulse-on time, supply current, gap voltage, and powder concentration have been identified to have a major influence on the machining characteristics like material removal rate (MRR), tool electrode wear rate (TWR), and surface roughness (SR). However, a lot of studies have been made to optimize the performance of these parameters on different materials [104][105][106][107][108][109][110][111].…”

Section: Overview Of Electrical Discharge Machiningmentioning

confidence: 99%

A Review of Additive Mixed-Electric Discharge Machining: Current Status and Future Perspectives for Surface Modification of Biomedical Implants

Aliyu

Rani

Ginta

et al. 2017

Advances in Materials Science and Engineering

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Surface treatment remained a key solution to numerous problems of synthetic hard tissues. The basic methods of implant surface modification include various physical and chemical deposition techniques. However, most of these techniques have several drawbacks such as excessive cost and surface cracks and require very high sintering temperature. Additive mixed-electric discharge machining (AM-EDM) is an emerging technology which simultaneously acts as a machining and surface modification technique. Aside from the mere molds, dies, and tool fabrication, AM-EDM is materializing to finishing of automobiles and aerospace, nuclear, and biomedical components, through the concept of material migrations. The mechanism of material transfer by AM-EDM resembles electrophoretic deposition, whereby the additives in the AM-EDM dielectric fluids are melted and migrate to the machined surface, forming a mirror-like finishing characterized by extremely hard, nanostructured, and nanoporous layers. These layers promote the bone in-growth and strengthen the cell adhesion. Implant shaping and surface treatment through AM-EDM are becoming a key research focus in recent years. This paper reports and summarizes the current advancement of AM-EDM as a potential tool for orthopedic and dental implant fabrication. Towards the end of this paper, the current challenges and future research trends are highlighted.

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Section: Overview Of Electrical Discharge Machiningmentioning

confidence: 99%

A Review of Additive Mixed-Electric Discharge Machining: Current Status and Future Perspectives for Surface Modification of Biomedical Implants

Aliyu

Rani

Ginta

et al. 2017

Advances in Materials Science and Engineering

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show abstract

“…20,21 GAs use biologically inspired methods like genetic inheritance, natural selection, mutation, and crossover. The solution of an optimization with GA begins with a set of ability solutions or chromosomes inside the character of bit strings, which might be randomly selected.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Evaluation of the surface integrity in the milling of a magnesium alloy using an artificial neural network and a genetic algorithm

Madhesan¹,

Venkatesan²,

Kaviarasan³

2018

Mater. Tehnol.

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Magnesium alloys are advanced, light materials used widely in industries and milling is one of the material removal processes that are extensively used. In this present study, the experimental work has been carried out based on a Box-Behnken design by mainly considering three factors, i.e., cutting speed, feed, and depth of cut. The first part of in this study, the effects of Response Surface methodology (RSM) and Artificial Neural Network (ANN) models were evaluated and compared. The RSM and ANN models provide the average error of 2.40 % and 1.52 %, respectively, it recommends that ANN is a more efficient methodology for the prediction of the optimal output response than RSM. The predicted model has been coupled with evolutionary optimization technique genetic algorithm (GA) to determine the optimum cutting parameters to attain the minimal surface roughness with respect to the wide ranges of machining parameters and GA suggest the surface roughness of 1.0926 μm with the optimized machining parameters. The evaluation of these observations proves that the proposed methods are capable of determining the optimum machining parameters for modern materials. Keywords: artificial neural network, genetic algorithm, magnesium alloy, milling, optimization Zlitine na osnovi magnezija (Mg) so napreden lahek material, ki se pogosto uporablja v razli~nih vejah industrije. Njegova mehanska obdelava z rezkanjem je ena od najpogostej{ih metod odstranjevanja materiala za dokon~no oblikovanje razli~nih industrijskih izdelkov. V predstavljeni {tudiji avtorji opisujejo eksperimentalno delo izvedeno na osnovi Box-Behnkenovega dizajna z upo{tevanjem treh faktorjev: rezalne hitrosti, pomika in globine reza. V prvem delu te {tudije so avtorji ovrednotili in primerjali u~inke dveh uporabljenih modelov: metodologije reakcije povr{ine (RSM; angl.: Response Surface Methodology) in umetne nevronske mre`e (ANN, angl: Artificial Neural Network). RSM in ANN modela sta predvidela povpre~no 2,40 % oz. 1,52 % napako. To pomeni, da je ANN u~inkovitej{a methodologija za napoved optimalne (reakcije) storilnosti. Model za prognozo so avtorji zdru`ili z evolucijsko optimizacijsko tehniko genetskih algoritmov (GA) in na ta na~in dolo~ili optimalne rezalne parametre za dosego minimalne povr{inske hrapavosti v obmo~ju dokaj {iroko izbranih parametrov mehanske obdelave. GA napoveduje povr{insko hrapavost 1.0926 μm pri optimiziranih parametrih mehanske obdelave. Ovrednotenje teh opazovanj je potrdilo da, je s predlaganimi metodami mo`no dolo~iti optimalne parametre mehanske obdelave modernih materialov. Klju~ne besede: umetna nevronska mre`a, genetski algoritem, zlitina na osnovi Mg, rezkanje, optimizacija

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“…In experiments surface roughness was determined by using contact type surf coder and kerf width was determined through video measuring system. Through multi objective optimization technique grey relational theory, the optimized value was obtained for surface roughness and kerf width and through Taguchi optimization technique; optimized value was obtained individually [20]. P. Gupta et.…”

Section: Literature Reviewmentioning

confidence: 99%

“Research Work for Optimization and Effect of WEDM Cutting Process Parameters on Performance Measures- a Literature Review”

Kumar¹

2017

IJRASET

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Analysis of Process Parameters in Wire EDM with Stainless Steel Using Single Objective Taguchi Method and Multi Objective Grey Relational Grade

Cited by 162 publications

References 6 publications

A Review of Additive Mixed-Electric Discharge Machining: Current Status and Future Perspectives for Surface Modification of Biomedical Implants

A Review of Additive Mixed-Electric Discharge Machining: Current Status and Future Perspectives for Surface Modification of Biomedical Implants

Evaluation of the surface integrity in the milling of a magnesium alloy using an artificial neural network and a genetic algorithm

“Research Work for Optimization and Effect of WEDM Cutting Process Parameters on Performance Measures- a Literature Review”

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