A dual response surface-desirability approach to process modeling and optimization of Al2O3 powder-mixed electrical discharge machining (PMEDM) parameters

Assarzadeh, S.; Ghoreishi, M.

doi:10.1007/s00170-012-4115-2

Cited by 103 publications

(46 citation statements)

References 39 publications

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“…However, an increase in pulse-on time increased the surface roughness significantly, whereas gap voltage increment had less effect on the surface roughness. This was justified by the previous studies [17][18][19][20]. Furthermore, higher pulseon time increased the plasma channel diameter which minimised both energy density and impulsive force.…”

Section: Effect On Average Surface Roughnessmentioning

confidence: 74%

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Electro-discharge machining of alumina: Investigation of material removal rate and surface roughness

Ali¹,

Moudood²,

Maleque³

et al. 2017

J. MECH. ENG. SCI.

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Alumina is a non-conductive ceramic material which can meet the high demand of industrial applications due to its excellent physical and chemical properties. However, machining of alumina is not possible by using the conventional machining methods due to its inherent brittleness. Recently, electro-discharge machining has been used for structuring alumina with assisting electrode to initiate the spark between the conductive tool electrode and the non-conductive work piece material. However, the effects of process parameters on material removal rate and surface roughness have not been investigated to formulate mathematical models. This study dealt with developing models for material removal rate and surface roughness correlating three process parameters which are peak current, pulse-on time and gap voltage using response surface methodology. The models were verified with 7% error between the results of empirical models and the experimental values.

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Section: Effect On Average Surface Roughnessmentioning

confidence: 74%

“…The reduction in impulsive force resulted in poor flushing of the melted debris which eventually formed a recast layer on the EDMed surface [2,21]. However, low pulse-on time with moderate peak current produced a plasma channel with higher energy density which can create a less rough surface [19,20].…”

Section: Effect On Average Surface Roughnessmentioning

confidence: 99%

Electro-discharge machining of alumina: Investigation of material removal rate and surface roughness

Ali¹,

Moudood²,

Maleque³

et al. 2017

J. MECH. ENG. SCI.

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show abstract

“…relations of a system under study were known (e.g. Dabbas et al 2003;Henkenjohann et al 2005), or their regression equations can be calculated prior to the application of the desirability function (Assarzadeh and Ghoreishi 2013). Therefore, these approaches could not be applied if the process analytical model is unknown, which is usually the case for new, complex processes.…”

Section: Multiresponse Optimisation Based On Desirability Function Anmentioning

confidence: 98%

Review of Multiresponse Process Optimisation Methods

Šibalija

Majstorovic

2015

Advanced Multiresponse Process Optimisation

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The review of methods used for multiresponse process parameter design and similar multiobjective optimisation problems is discussed in this chapter, implying the following classification: (1) conventional methods based on statistical or mathematical techniques: (i) experimental design techniques (response surface methodology, Taguchi's robust parameter design and related approaches), and (ii) iterative mathematical search techniques; (2) non-conventional methods based on artificial intelligence techniques: (i) fuzzy logic, (ii) artificial neural networks, (iii) metaheuristic search techniques (genetic algorithm, simulated annealing, particle swarm optimisation, ant colony optimisation, tabu search, and recent evolutionary algorithms such as artificial bee colony algorithm, biogeography-based optimisation, and teaching-learning-based optimisation), and (iv) expert systems.

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“…The goal of optimization is to find a set of conditions that will meet all the goals. It is not important that the desirability value is 1.0 as the value is completely dependent on how closely the lower and upper limits are set relative to the actual optimum [43].…”

Section: Desirability Function (Df) Approach-mentioning

confidence: 99%

Optimization of Electro Discharge Machining of Superalloys and Composites: A Review

2015

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

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Abstract-Electro discharge machining (EDM) processis a well-established advanced machining process for producing complex geometry with close tolerances in these materials that are extremely difficult-to-machine by conventional machining processes. EDM process offers a better alternative or sometimes the only alternative for generating accurate three dimensional complex shapes of macro, micro and nano features in difficult-tomachine materials among other advanced machining processes. The success of the EDM process depends upon the selection of appropriate process parameters. The selection of preferred process parameters plays a significant role to ensure quality of product and to reduce the machining cost in computer controlled machining process. Hence, Optimization methods applied for machining process are necessary for continuous improvement of the machining process. The objective of this paper is to offer comprehensive knowledge concerning the optimization of electro discharge machining of superalloys and composites materials.

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A dual response surface-desirability approach to process modeling and optimization of Al2O3 powder-mixed electrical discharge machining (PMEDM) parameters

Cited by 103 publications

References 39 publications

Electro-discharge machining of alumina: Investigation of material removal rate and surface roughness

Electro-discharge machining of alumina: Investigation of material removal rate and surface roughness

Review of Multiresponse Process Optimisation Methods

Optimization of Electro Discharge Machining of Superalloys and Composites: A Review

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