Multi-attribute decision making for green electrical discharge machining

Sivapirakasam, S.P.; Mathew, Jose; Surianarayanan, M.

doi:10.1016/j.eswa.2011.01.026

Cited by 132 publications

(50 citation statements)

References 15 publications

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“…Using kerosene as dielectric will discharge large amounts of solid, liquid and gaseous wastes, which result in serious environmental problems. These toxic substances can enter the body through inhalation and skin contact [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…EDM is a non-traditional machining method, which is extensively used in machining hard, highstrength, complex geometry shape and temperature-resistant materials in a contactless manner. The material is melted and evaporated by the heat between electrode tool and work piece [3][4][5][6][7][8][9]. Recently, EDM technology made it widely used in machining the key parts such as aerospace and aeronautical components.…”

Section: Introductionmentioning

confidence: 99%

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Experimental study on green electrical discharge machining in tap water of Ti–6Al–4V and parameters optimization

Tang

2013

Int J Adv Manuf Technol

102

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Ti-6Al-4V is widely used in the aerospace, automobile, and biomedical fields, but is a difficult-to-machine material. Electrical discharge machining (EDM) is regarded as one of the most effective approaches to machining Ti-6Al-4V alloy, since it is a non-contact electro-thermal machining method, and it is independent from the mechanical properties of the processed material. This paper aims to combine grey relational analysis and Taguchi methods to solve the problem of EDM parameters optimization. From the viewpoint of health and environment, tap water as working fluid has good working environment, since it does not release harmful gas. The process parameters include discharge current, gap voltage, lifting height, negative polarity and pulse duty factor. The electrode wear ratio (EWR), material removal rate (MRR) and surface roughness (SR) as objective parameters are chosen to evaluate the whole machining effects. Experiments were carried out based on Taguchi L 9 orthogonal array and grey relational analysis, and then verified the results through a confirmation experiment. Compared the machining parameters A 1 B 1 C 3 D 2 with A 1 B 2 C 2 D 2 , MRR increased from 1.28 mm 3 /min to 2.38 mm 3 /min, EWR decreased from 0.14 to 0.10 mm 3 /min and SR decreased from Ra 2.37 μm to Ra 1.93 μm. The process parameters sequenced in order of relative importance are: the ratio of pulse width to pulse interval, discharge current, lifting height and gap voltage. The results showed that using tap water machining Ti-6Al-4V material can obtain high MRR, decrease the machining cost and have no harmful to the operators and the environment.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental study on green electrical discharge machining in tap water of Ti–6Al–4V and parameters optimization

Tang

2013

Int J Adv Manuf Technol

102

View full text Add to dashboard Cite

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“…Sivapirakasam et al [27] developed a combination of Taguchi and FTOPSIS methods to solve multi-response parameter optimization problems in green manufacturing. Also, ANN's applied to many civil engineering applications such as drying shrinkage [28], ready mixed concrete delivery [29], slump model [30], concrete durability [31], mechanical behavior of concrete at high temperatures [32][33][34], workability of concrete with metakaolin and fly ash [35,36], and the long term effect of fly ash and silica fume on compressive strength [37], predicting comprehensive strength and slump for high strength concrete (HSC) [38], drying shrinkage of concrete [39], estimation of compressive strength of self-compacting concrete [40].…”

Section: Introductionmentioning

confidence: 99%

Hybridizing a fuzzy multi-response Taguchi optimization algorithm with artificial neural networks to solve standard ready-mixed concrete optimization problems

Şimşek¹,

İç²,

Şimşek³

2016

IJCIS

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In this study, a fuzzy multi-response standard ready-mixed concrete (SRMC) optimization problem is addressed. This problem includes two conflicting quality optimization objectives. One of these objectives is to minimize the production cost. The other objective is to assign the optimal parameter set of SRMC's ingredient to each activity. To solve this problem, a hybrid fuzzy multi-response optimization and artificial neural network (ANN) algorithm is developed. The ANN algorithm is integrated into the multi-response SRMC optimization framework to predict and improve the quality of SRMC. The results show that fuzzy multi-response optimization model is more effective than crisp multi-response optimization model according to final production cost. However, the ANN model also gave more accurate results than the fuzzy model considering the regression analysis results.: Standard ready-mixed concrete; Multi-response optimization; Taguchi method; Fuzzy TOPSIS; Artificial neural networks.

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“…The regression relationship between tool wear ratio and the process inputs are therefore established, and the results are presented in Table 4. In the regression model, the contributing factors for prediction of the tool wear ratio (TWR) are the parameters feed rate, capacitance, voltage and an interaction voltage 3 . The other two-factor and three-factor interactions associated with the current micro-EDM process those are assumed to influence the process are found to have no significant effect in prediction of the tool wear ratio in the model.…”

Section: Regression Analysis Of Tool Wear Ratiomentioning

confidence: 99%

“…In the past, many researchers have attempted to machine different kinds of 'difficult-to-cut' materials using spark machining-based technologies [1][2][3][4][5][6][7][8][9]. The endeavors primarily involved optimization of the processing conditions in macro electrical discharge machining [1,[4][5][6][7], micro-electrical discharge machining [2], green electrical discharge machining [3] and other hybrid processes using the spark machining principles [8,9]. A few researchers [10][11][12] have focused their attention on using alternate methods for increasing the material removal rate through chemical and mechanical actions using application of powders or surfactants.…”

Section: Introductionmentioning

confidence: 99%

An Influence of Parameters of Micro-electrical Discharge Machining on Wear of Tool Electrode

Puthumana¹

2017

Archive of Mechanical Engineering

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To achieve better precision of features generated using the micro-electrical discharge machining (micro-EDM), there is a necessity to minimize the wear of the tool electrode, because a change in the dimensions of the electrode is reflected directly or indirectly on the feature. This paper presents a novel modeling and analysis approach of the tool wear in micro-EDM using a systematic statistical method exemplifying the influences of capacitance, feed rate and voltage on the tool wear ratio. The association between tool wear ratio and the input factors is comprehended by using main effect plots, interaction effects and regression analysis. A maximum variation of four-fold in the tool wear ratio have been observed which indicated that the tool wear ratio varies significantly over the trials. As the capacitance increases from 1 to 10 nF, the increase in tool wear ratio is by 33%. An increase in voltage as well as capacitance would lead to an increase in the number of charged particles, the number of collisions among them, which further enhances the transfer of the proportion of heat energy to the tool surface. Furthermore, to model the tool wear phenomenon, a regression relationship between tool wear ratio and the process inputs has been developed.

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Multi-attribute decision making for green electrical discharge machining

Cited by 132 publications

References 15 publications

Experimental study on green electrical discharge machining in tap water of Ti–6Al–4V and parameters optimization

Experimental study on green electrical discharge machining in tap water of Ti–6Al–4V and parameters optimization

Hybridizing a fuzzy multi-response Taguchi optimization algorithm with artificial neural networks to solve standard ready-mixed concrete optimization problems

An Influence of Parameters of Micro-electrical Discharge Machining on Wear of Tool Electrode

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