Issue 
Manufacturing Rev.
Volume 9, 2022



Article Number  13  
Number of page(s)  10  
DOI  https://doi.org/10.1051/mfreview/2022010  
Published online  27 June 2022 
Research Article
Multicriteria decision making in electrical discharge machining with nickel coated aluminium electrode for titanium alloy using preferential selection index
^{1}
Hanoi University of Industry, No. 298, CauDien Street, Bac TuLiem District, Hanoi, Vietnam
^{2}
Thai nguyen University of Technology, Thai nguyen, Vietnam
^{3}
Department of Mechanical Engineering Department, RIT, Rajaramnagar Affiliated to SUK, Maharashtra, India
^{*} email: ngocvu@tnut.edu.vn
Received:
9
January
2022
Accepted:
10
April
2022
In the present scenario, great effort is expended to improve the machining process by adopting multicriteria decision making in electrical discharge machining (EDM). In this research article, an attempt was made to optimize the process parameters of EDM with Nickel Coated Aluminium Electrode for machining Titanium Alloy using Preference Selection Index (PSI). The experimental work were performed using Taguchi based L16 orthogonal to solve multiobjective optimization problem. The current (I), voltage (U) and pulse on time (T_{on}) were used as input response variables for investigation process while material removal rate (MRR) and tool wear rate (TWR) were selected as performance measures. The experimental results show that set of optimized parameters of the multiobjective optimization problem in EDM with nickel coated aluminium electrode could improve the machining with better surface measures with less deviation from the prediction. The combination between PSI and Taguchi method reduced and saved significantly the experimental time and cost and increased accuracy for optimization process.
Key words: EDM / PSI / Taguchi
© N.H. Phan et al., Published by EDP Sciences 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1 Introduction
Electrical discharge machining (EDM) is widely used among all nontraditional machining method for the machining of moulds [1,2]. It is highly effective with complex shapes made from materials that are difficult to achieve using traditional machining methods [3,4]. The machining productivity and surface quality are main limitations of such process [5]. The large number of process parameters with wide range makes high difficult to optimize process parameters in EDM [6]. Hence the optimization for improving machining productivity and machined surface quality in EDM is still attracting the attention of many researches and experts [7–10]. Using coated electrode in EDM is a new research direction, its results are very feasible in practice and industrial manufacturing [11,12]. The results of studies in this direction are few. The optimization algorithms can enhance the performance measures in manufacturing processes [13].
The invention of newer electrode materials with improved mechanical and chemical properties can enhance the productivity, quality of machined surface and accuracy machining in EDM. The utilization of coated electrodes in EDM process is still an engaging research area to overcome the limitations of this machining method. The microhardness (HV) of the machined surface has been enhanced by 163% compared to the base material layer [14]. As compared to the uncoated electrode, the microscopic cracks formed on the machining surface in EDM using CuMWCNT coated electrode could be significantly reduced. Compared to the EDM using uncoated electrode, the use of a 5 micrometer coating with silver on the Cu in EDM electrode surface resulted in a significant increase in MRR of 26.8%, a sharp drop of TWR by 25%, dimensional accuracy and surface quality is significantly improved [15]. Using electrodes with different coating materials, it will give very different machining efficiency in EDM. Compared to the nickel coated electrode, the TWR in EDM using diamondnickel coated electrode has been significantly enhanced [16]. And the diameter size accuracy in EDM using coated electrode is higher than it with uncoated electrode. TiN and TiAlN were used to coat the surface of Cu electrode in EDM [17]. Compared to the uncoated Cu electrode, the machining efficiency of the coated electrode is better, and the TiN coated electrode is better than the TiAlN coated electrode. And EDM using TiN coated electrode is suitable for finishing. Coating material has been found on the machined surface layer, which is capable of improving the surface layer after EDM using coated electrodes [18]. The use of coated electrodes has resulted in a drop in the cost of the electrode, and this will contribute in improving the economics of the EDM machining process [19]. Electrodes coated in EDM are a new technology solution, which requires further research in this area including optimization of technological parameters, the types of coating materials used, coating thickness on electrode surface, etc. [20]. The material is used to coat the surface of the electrode, it alters the properties of mechanical and physical chemistry of the material layer of electrode surface. It can affect the process of spark formation in the discharge gap. It will affect the selection of technology parameters to enhance the machining process in EDM. Hence, it is essential to determine optimal technological factors for each new material coated on the electrode surface for improving machining efficiency in EDM.
Recently, some researches have shown that combining Taguchi with other methods such as GRA (Grey Relational Analysis), TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), PSO (Particle Swarm Optimization), etc. can simultaneously optimize multiobjective in EDM [21]. Taguchi – GRA was used to simultaneously optimize the MRR, TWR and overstriation expenditures in µEDM [22,23]. However, the study of simultaneous optimization of MRR and surface roughness (R_{a}) in EDM by Taguchi – TOPSIS has higher efficiency than it using Taguchi – GRA [24].
Among the researches of optimization processes in EDM, some quality characteristics in EDM using Taguchi – TOPSIS also presented in [25]. The quality parameters are optimized including MRR, R_{a}, dimensional accuracy. The results showed that among the process parameters (such as I, U, T_{on}), U has the strongest influence (42.42%) and T_{on} has lowest influence (11.13%). In multiobjective optimization, TOPSIS is a simple and powerful method [26]. At the same time, this method allows to consider quantitative and qualitative factors, so this solution can be approached in favor of multiobjective decision which is more favorable [27]. The quality parameters in μEDM were simultaneously optimized by Topsis [28–30]. The influence of process parameters has been presented. Optimum results of productivity, surface quality and machining accuracy in EDM were determined by the TOPSIS method [31,32]. The value of the weights of the quality criteria taken in the jar is a limitation of PSI, which may not be suitable for many practical cases [33]. However, the mechanism of EDM using coated electrode is very new and its application is very small, so it is extremely difficult to determine its importance in practice [34,35]. Therefore, PSI is the appropriate solution for this study. This paper showed that the optimal results are better than some other methods such as Taguchi, GRA etc. [36]. The above research results have shown that the solutions used in the multiobjective optimization problem in EDM have achieved certain results [37]. However, the value of the weights of the defined criteria is difficult for optimization process. Recently, the PSI method has been introduced for multiobjective optimization in machining methods [38]. This method does not require determining the weight of the criteria, so the optimization problem will be solved much simpler. Few research results have shown that PSI method is a multiobjective decision solution with higher efficiency than that of TOPSIS, GRA, etc. [39,40]. TaguchiDEAR method can provide simplest methodology [41,42]. However, the accuracy is still to be enhanced. Whereas GRA could need knowledge of selecting proper design of experiments and Grey coefficient [43,44]. It was found that TOPSIS method, GRA and GRA Fuzzi can be suitable for thin film coated electrode EDM process [45,46].
It can be seen that the researches in the EDM field focusing on the application of titanium Nickel coated electrodes are very few and there are many problems that need to be studied, especially determining the optimal process parameters to increase productivity, quality and reduce cost of products in EDM. Based on above literature review, this paper studied on multicriteria decision making in EDM with Nickel coated Aluminium electrode for Titanium alloy material using PSI to find out optimized quality indicators including MRR and TWR. Process parameters including U, I, T_{on} were selected for optimization process. To reduce experimental time and cost and increase accuracy, Taguchi – PSI methods was used to design experimental and perform multiobjective optimization process. Section 2 is dealt with experimental methodology and Section 3 is discussed with interpretation of results. Section 4 is discussed with the derived conslusion for the experimental results.
2 Experimental methodology
2.1 Experimental setup
The CNCAG40L Machine (Sodick, Inc. USA) was used to perform the experiment with Titanium alloy (Ti6Al4V) material. Such technique can be proposed to manufacture complex shape mould and dies in manufacturing industries using EDM process. The characteristics of the mold steels are indicated in Table 1 with size of workpiece of 15 × 15 × 5 mm. Nickel coated Aluminum electrode was selected for investigation in the study. The coating for electrode can efficiently enhance the surface performance measures in EDM process [47]. The shape of electrode is cylindrical with 10 mm in diameter and 35 mm in length, as shown in Figure 1. Table 1 and Figure 2 shows EDAX of copper coating and it was evident that presence of copper material in coating. The dielectric solution used in the present study was HD1 oil [48]. This is the type of oil used quite commonly in the fields of pulse machining today in Vietnam. AJ 203 electronic balance (Shinko Denshi Co. LTD – Japan) was used to measure the weight of the workpiece and electrode before and after machining. The maximum weight that the scale can weigh is 200 g, with an accuracy of 0.001 g. The measurement were taken 3 times of measurements on each test sample and the average value were considered as final values to enhance the measurement accuracy [47].
Fig. 1 Thin film nickel coated electrode. 
Fig. 2 EDAX Report of nickel coating tool electrode. 
Elements in nickel coating.
2.2 Build the experimental matrix by Taguchi method
The choice of the experimental design matrix in Taguchi depends on the number of technological parameters and its levels examined. In this study, three process parameters (U, I and T_{on}) and the levels of each parameter have been selected, as shown in Table 2. And the degrees of freedom of the experimental matrix are 9. Thus, Taguchi's experimental design table is L16 [11]. The experimental matrix and results are shown as Table 3 and Figure 3.
Fig. 3 Images of electrode and workpiece. (a) Electrode surface; (b) Workpiece surface. 
Process parameters in the experiment.
Experimental results.
2.3 Multiobjective optimization using PSI
The main steps of the PSI for solving MCDM problems include several steps, as follows:
 Step 1
Determine the objective of the problem and select the evaluation criteria to ensure the objective, and select the empirical matrix related to the decisionmaking problem under consideration.
 Step 2
Build the initial decision matrix from the initially selected criteria. If the number of experiments is m and the number of indicators is n, then the decision matrix of mxn can be represented by equation (1) [40]: (1)
 Step 3
In multiobjective problems, it is required to make the values of the criteria nonunit. Therefore, these values will be converted to 0 and 1, and this conversion process is called normalization. If the indicator is larger, then it is normalized according to the formula (2a) [40]: (2a)
If the indicator is smaller, it is better, it will be normalized according to the equation (2b): (2b)
where x_{ij} is values of the indicators at row i and coluum j (i = 1, 2, 3, … , m and j = 1, 2 … , n). Decision matrix is normalized by equation (2a) and (2b) according to objective of problem.
 Step 4
Compute the mean value of the normalized data (N): In this step, the average value of the normalized indicators can be calculated by equation (3), as follows [40]: (3)
 Step 5
Compute the preference variation value (ϕ_{j}): The optional variable value among indicators is calculated using equation (4) [40]: (4)
 Step 6
Determine the deviation in preference value (Ω_{j}): Determine the deviation of the value of the priority relating to each criterion using equation (5) [40]: (5)
 Step 7
Compute the overall preference value (W_{j}): In this step, the overall priority value is determined for each criterion using equation (6) [40]: (6)
In addition, the sum of the overall priority values of all criteria must satisfy the equation (7) [40]: (7)
 Step 8
Compute the preference selection index (θ_{j}): The preference selection index was calculated for each experiment using equation (8) [40]: (8)
 Step 9
Select the appropriate alternative for the given application: Based on the priority index value to rank, the ranking must be done according to the descending value of θ_{j}. The experiment with the largest value θ_{j} is the greatest, it will be the best solution (optimal solution).
2.4 Analyzing and optimizing
Analyze experimental results: The experiment with the highest value of S/N coefficient will give the optimal result that is least affected by noise. S/N is used to determine the level for optimal output. The S/N coefficients of the outputs are determined as follows [43]:

where
MSD_{HB} – average square deviation; r – number of the tests in an experiment (repeating times); y_{i} – experimental values; – standard value is the best: (10)
where
MSD_{NB} – average square deviation; y_{0} – standard value or target value.

where
MSD_{LB} – average square deviation; – sum of the squares of all the results of each experiment.
Optimizing outputs: Optimized value (µ) is estimated by the strong influence parameters and is determined by equation (12), as follows: (12)
where – average value at levels A_{2}, B_{3}, C_{3}.
3 Results and discussion
3.1 Effect of process parameters on quality criterias
Effect of current (I): MRR and TWR also increase when curent (I) increases, as shown in Figure 4. MRR and TWR change significantly when curent (I) changes. Comparing with I = 10 A, increase of MRR was 306.7% and TWR is 196.0% at I = 40 A. This problem comes from the increase of current (I), therefore, energy which was used to machine increases. So, energy of electrical sparks increases significantly. Therefore, electrode and workpiece material which are melt and vaporized also increase. Comparing EDM using Aluminium (Al) electrode, effects of curent (I) to quality criteria in EDM using coated electrode is better and MRR in EDM using coated Al electrode is higher and TWR is smaller [48]. This shows that Nickel coated layer affects well to machining efficiency using EDM.
Effect of voltage (U): Effects of voltage (U) to MRR and TWR is the same effects of curent (I), as shown in Figure 5. However, change of MRR and TWR by voltage (U) is lower. Comparing with U = 40 V, increase of MRR and TWR at U = 55 V is 34.6% and 72.4%, respectively. This problem comes from the change of voltage (U), it will affect to energy of sparks and energy which break the insulation of the dielectric solution, and this energy increases when voltage (U) increases. However, increase of voltage (U) afects badly to EDM machining process using coated electrode because increase of MRR is smaller than increase of TWR [48]. So, electrode life will be decreased, machining accuracy is low. Results showed that Nickel is affected significantly.
Effect of pulse on time (T_{on}): Figure 6 shows that T_{on} afects to MRR and TWR in EDM using coated electrode. The results also show that MRR and TWR decrease significantly when T_{on} increases. Comparing with T_{on} = 10 µs, decrease of MRR and TWR in EDM using coated electrode are 21.4% and 23.2% comparing with T_{on} = 1500 µs, respectively. This problem comes from the increase of T_{on}, therefore, particles removing time and dielectric solution affected by sparks are reduced. They make instability in machining using EDM, short circuit cycle and the phenomenon of electric arc discharge appear with the larger frequency. Therefore, the energy of the useful sparks is reduced. And this is the main cause which reduces machining productivity, and TWR is also reduced at the same time. Compared with EDM using Al electrode, MRR and TWR were more strongly affected by the change of T_{on} with Nickel coated electrode [48] because the Nickel coated layer is strongly influenced by T_{on}.
Fig. 4 Effect of I on MRR and TWR in EDM using coated electrode. 
Fig. 5 Effect of U on MRR and TWR in EDM using coated electrode. 
Fig. 6 Effect of T_{on} on MRR and TWR in EDM using coated electrode. 
3.2 Multicriteria decision
Calculation process using PSI:
 Step 1
With the goal of simultaneously improving productivity and surface quality, two criteria including MRR and TWR will be selected for investigation (MRR will be increased; TWR will be reduced). The experimental results of the two indicators were surveyed according to Taguchi's L16 matrix, as shown in Table 2.
 Step 2
Building the matrix of the investigated criteria, according to equation (1), we have: (13)
 Step 3
Standardization of the indicators: MRR is normalized according to equation (2a) and TWR is normalized according to equation (2b), the results are shown in Table 4.
 Step 4 to step 8
The results are calculated according to the equations (3)–(8). The values of the calculated results are shown in Table 5.
 Step 9
Based on index calculation results of PSI, it shows that the 6_{th} experiment will give the largest value, as shown in Figure 7. This will be the experiment that gives the most reasonable results with the set of process parameters. Optimal values are follows: U = 45 V, T_{on} = 100 µs, I = 20 A.
Determining the optimal set of parameters
The results of the S/N analysis of θ_{j} are shown in Figure 8. The results show a reasonable set of process parameters including U = 45 V, I = 30 A, T_{on} = 500 μs. The value of the quality criterias at optimal conditions is determined according to (12), and the exact optimal value is determined by equation (14). The accuracy of the calculated results and the experimental results are consistent and the maximum error of the calculated results is 9.01%, as shown in Table 6.
Normalization results.
The values of conversion parameters and priority index.
Fig. 7 Ranking PSI index. 
Fig. 8 Chart of main effects for S/N ratios of θ_{j}. 
Confirmation of experimental results of PSI method.
3.3 Machined surface in EDM with optimization processes
Electrical Discharge Machining (EDM) is a finished machining method in shaped operrations of a product. However, machined surface in EDM often appears a lot of defects such as transformation of worrkpiece surface layer comparing background material layer, micro cracks, particle adhesing on surface and empty holes appearing on surface layer. The cause comes from machining mechanism of EDM. This method uses heat energy of sparks to melt and vapour workpiece and electrode material. Therefore, machined surface in EDM is often machined lastly using grinding or polishing opration. So, machined surface quality in EDM using coated electrode will affect significantly to cost and time of the last finished machining operation. Analyzing results of machined surface in EDM using Nickel coated electrode is shown in Figures 9–11. Craters, pores and micro cracks are distributed randomly on machined surface, as shown in Figure 9. Micro cracks are formed by hight heat of sparks which causes the material of workpiece surface to evaporate and melt, and this material layer is cooled rapidly by dielectric solution. In EDM, pores are formed by air bubbles which exist in dielectric solution, it is intrusive on workpiece surface in machining process. The particles adhere to the machined surface, they include two types which are the fastened adhesion and the weak adhesion particles. The fastened adhesion particles are formed by melting and vapouring of workpiece material and a part of electrode material and they are cooled rapidly by dielectric solution. They are recasted imtermediately on machied surface. The weak adhesion particles (Globules) are formed by melting and vapouring of particles of workpiece and electrode material which are removed out surface of workpiece and electrode but they are pushed out spark gap by dielectric solution. There are a lot of empty holes on machined surface, so its structure is porous, as shown in Figure 10. This affects to work ability of machined surface. A white layer is also formed on machined surface, as shown in Figure 11. It is necessary to remove this layer out the surface of workpiece using finished machining methods. Cut depth of finished machining methods has to be larger than thickness of white layer (≥9 µm).
Fig. 9 Topography of machined surface. 
Fig. 10 Defects on the machined surface. 
Fig. 11 White layer thickness of machined surface. 
4 Conclusions
In present study, an attempt was made to optimize EDM process with Nickel Coated Aluminium Electrode for machining Titanium Alloy using PSI (Preference Selection Index). The current (I), voltage (U) and pulse on time (T_{on}) were used as input variables for investigation process. From the experimental results, the following conclusions were drawn:
Current (I) is the parameter which affects significantly to MRR and TWR in EDM using coated, U and T_{on} is parameter which affects insignificantly.
The optimal set of process parameters was found as U = 45 V, T_{on} = 500 µs, I = 30 A. The optimal indicator values were found as MRR = 0.076 mm^{3}/min and TWR = 0.016 mm^{3}/min with deviation of 9.09%.
It has proved that PSI is an effective method to solve multiobjective optimization in the field of EDM in particular and other machining technologies.
Another research direction which needs attention is the optimal methods which needs to give results with high accuracy and are suitable for production practice.
The surface quality after EDM using coated electrode is good but amount of material removal during the process is less (⋍7.79 µm).
Acknowledgments
The work described in this paper was supported by Thai Nguyen University of Technology (TNUT), Thainguyen, Vietnam.
References
 K.M. Vijay, S.A. Man, MicroEDM multiple parameter optimization for Cp titanium, Int. J. Adv. Manuf. Technol. 89 (2017) 897–904 [CrossRef] [Google Scholar]
 N.H. Phan et al., Material removal rate in electric discharge machining with aluminum tool electrode for Ti6Al4V titanium alloy, Adv. Eng. Res. Appl. (2021) https://doi.org/10.1007/9783030647193_58 [Google Scholar]
 M. Chausov, A. Pylypenko, V. Berezin, K. Volyanska, P. Maruschak, V. Hutsaylyuk, L. Markashova, S. Nedoseka, A. Menou, Influence of dynamic nonequilibrium processes on strength and plasticity of materials of transportation systems, Transport 33 (2018) 231–241 [Google Scholar]
 N.H. Phan et al., Tool wear rate analysis of uncoated and AlCrNi coated aluminum electrode in EDM for Ti6Al4 V titanium alloy, Adv. Eng. Res. Appl. (2021) https://doi.org/10.1007/9783030647193_91 [Google Scholar]
 M. Durairaj, D. Sudharsun, N. Swamynathan, Analysis of process parameters in wire EDM with stainless steel using single objective taguchi method and multi objective grey relational grade, Proc. Eng. 64 (2013) 868–877 [CrossRef] [Google Scholar]
 N. Pragadish, M.P. Kumar, Optimization of dry EDM process parameters using grey relational analysis, Arab. J. Sci. Eng. 41 (2016) 4383–4390 [CrossRef] [Google Scholar]
 M.A. Ilani, M. Khoshnevisan, Mathematical and physical modeling of FESEM surface quality surrounded by the plasma channel within Al powdermixed electrical discharge machining of Ti6Al4V. Int. J. Adv. Manuf. Technol. 112 (2021) 3263–3277 [CrossRef] [Google Scholar]
 A. Taherkhani, M.A. Ilani, F. Ebrahimi et al., Investigation of surface quality in Cost of Goods Manufactured (COGM) method of μAl_{2}O_{3} powdermixedEDM process on machining of Ti6Al4V, Int. J. Adv. Manuf. Technol. 116 (2021) 1783–1799 [CrossRef] [Google Scholar]
 M.A. Ilani, M. Khoshnevisan, Powder mixedelectrical discharge machining (EDM) with the electrode is made by fused deposition modeling (FDM) at Ti6Al4V machining procedure, Multisc. Multidiscip. Model. Exp. Des. 3 (2020) 173–186 [CrossRef] [Google Scholar]
 M.A. Ilani, M. Khoshnevisan, Study of surfactant effects on intermolecular forces (IMF) in powdermixed electrical discharge machining (EDM) of Ti6Al4V, Int. J. Adv. Manuf. Technol. 116 (2021) 1763–1782 [CrossRef] [Google Scholar]
 N.H. Phan, P.V. Dong, T. Muthuramalingam, N.V. Thien, H.T. Dung, T.Q. Hung, N.V. Duc, N.T. Ly, Experimental investigation of uncoated electrode and PVD AlCrNi coating on surface roughness in electrical discharge machining of Ti6Al4V, Int. J. Eng. Trans. A 34 (2021) 928–934 [Google Scholar]
 N.H. Phan, P. Van Dong, H.T. Dung et al., Multiobject optimization of EDM by TaguchiDEAR method using AlCrNi coated electrode, Int. J. Adv. Manuf. Technol. (2021). https://doi.org/10.1007/s00170021070323 [Google Scholar]
 G. Sakthivel, D. Saravanakumar, T. Muthuramalingam, Application of failure mode and effects analysis in manufacturing industry – an integrated approach with FAHP – FUZZY TOPSIS and FAHPFUZZY VIKOR, Int. J. Product. Qual. Manag. 24 (2018) 398–423 [CrossRef] [Google Scholar]
 P. Mandal, S. Chandra Mondal, Surface characteristics of mild steel using EDM with CuMWCNT composite electrode [Google Scholar]
 R. Jothimurugan, K.S. Amirthagadeswaran, J. Daniel, Performance of silver coated copper tool with keroseneservotherm dielectric in EDM of Monel 400TM, J. Appl. Sci. 12 (2012) 999–1005 [CrossRef] [Google Scholar]
 Y. Liu, W. Wang, W. Zhang, F. Ma, D. Yang, Z. Sha, S. Zhang, Experimental study on electrodewear of diamondnickel coated electrode in EDM small hole machining, Adv. Mater. Sci. Eng. (2019) ID 7181237, 10 pages [Google Scholar]
 D.L. Panchal, S.K. Biradar, V.Y. Gosavi, Analysis of EDM process parameters by using coated electrodes, Int. J. Eng. Trends Technol. 41 (2016) [Google Scholar]
 A. Rashid, A. Bilal, C. Liu, M.P. Jahan, D. Talamona, A. Perveen, Effect of conductive coatings on microelectrodischarge machinability of aluminum nitride ceramic using onmachinefabricated microelectrodes, Materials 12 (2019) 3316 [CrossRef] [Google Scholar]
 T.R. Ablyaz, E.S. Shlykov, S.S. Kremlev, Coppercoated electrodes for electrical discharge machining of 38X2H2MA steel, Russ. Eng. Res. 37 (2017) 910–911 [CrossRef] [Google Scholar]
 E. Uhlmann, M. Langmack, R. Garn, D. Oberschmidt, J. Fecher, S.M. Rosiwal, R.F. Singer, Using diamond coated toolelectrodes for drilling micro holes with EDM, in Proceedings of the Euspen International Conference – Como (2011) [Google Scholar]
 M. Dastagiri, R.P. Srinivasa, P.M. Valli, TOPSIS, GRA methods for parametric optimization on wire electrical discharge machining (WEDM) process, in Design and Research Conference (AIMTDR–2016) College of Engineering (2016) [Google Scholar]
 S. Prabhu, B.K. Vinayagam, Multiresponse optimization of EDM process with nanofluids using TOPSIS method and genetic algorithm, Arch. Mech. Eng. 63 (2016) 45–71 [CrossRef] [Google Scholar]
 M. Durairaj, D. Sudharsun, N. Swamynathan, Analysis of process parameters in wire EDM with stainless steel using single objective Taguchi method and multi objective grey relational grade, Proc. Eng. 64 (2013) 868–877 [CrossRef] [Google Scholar]
 I. Nayak, J. Rana, A. Parida, Performance optimization in electro discharge machining using a suitable multiresponse optimization technique, Decis. Sci. Lett. 6 (2017) 283–294 [CrossRef] [Google Scholar]
 K.M. Vijay, S.A. Man, S. Suman, S. Narinder, MicroEDM multiple parameter optimization for Cp Titanium, Int. J. Adv. Manuf. Technol. 89 (2017) 897–904 [CrossRef] [Google Scholar]
 M. Himadri, M Kalipada, Optimization of machining condition in WEDM for titanium grade 6 using MOORA coupled with PCA – a multivariate hybrid approach, J. Adv. Manufactur. Syst. 16 (2017) 81–99 [CrossRef] [Google Scholar]
 K. Rajesh, K. Anish, P.G. Mohinder, S. Ajit, S. Neeraj, Multiple performance characteristics optimization for Al 7075 on electric discharge drilling by Taguchi grey relational theory, J. Ind. Eng. Int. 11 (2015) 459–472 [CrossRef] [Google Scholar]
 K. Pawan, Meenu, K. Vineet, Optimization of process parameters for WEDM of Inconel 825 using grey relational analysis, Decis. Sci. Lett. 7 (2018) 405–416 [Google Scholar]
 S. Tripathy, D.K. Tripathy, Multiattribute optimization of machining process parameters in powder mixed electrodischarge machining using TOPSIS and grey relational analysis, Eng. Sci. Technol. 19 (2016) 62–70 [Google Scholar]
 S.P. Sivapirakasama, J. Mathew, M. Surianarayanan, Multiattribute decision making for green electrical discharge machining, Exp. Syst. Appl. 38 (2011) 8370–8374 [CrossRef] [Google Scholar]
 A.P. Tiwary, B.B. Pradhan, B. Bhattacharyya, Application of multicriteria decision making methods for selection of microEDM process parameters, Adv. Manuf. 2 (2014) 251–258 [CrossRef] [Google Scholar]
 J. Huo, S. Liu, Y. Wang, T. Muthuramalingam, V. Ngoc Pi, Influence of process factors on surface measures on electrical discharge machined stainless steel using TOPSIS, Mater. Res. Express 6 (2019) 086507 [CrossRef] [Google Scholar]
 K.B. Rajesh, C.R. Bharat, Optimization the machining parameters by using VIKOR and entropy weight method during EDM process of Al–18% SiCp metal matrix composite, Decis. Sci. Lett. 5 (2016) 269–282 [Google Scholar]
 P.H. Nguyen, T.L. Banh, K.A. Mashood et al., Application of TGRAbased optimisation for machinability of highchromium tool steel in the EDM process, Arab. J. Sci. Eng. 45 (2020) 5555–5562 [CrossRef] [Google Scholar]
 V.S. Gadakh, Parametric optimization of wire electrical discharge machining using topsis method, Adv. Product. Eng. Manag. 7 (2012) 157–164 [CrossRef] [Google Scholar]
 R. Manivannan, K.M. Pradeep, Multiattribute decisionmaking of cryogenically cooled microEDM drilling process parameters using TOPSIS method, J. Mater. Manufactur. Process. 32 (2017) 209–215 [CrossRef] [Google Scholar]
 R. Manivannan, M.P. Kumar, Multiresponse optimization of MicroEDM process parameters on AISI304 steel using TOPSIS, J. Mech. Sci. Technol. 30 (2016) 137–144 [CrossRef] [Google Scholar]
 D. Petković, M. Madić, M. Radovanović, V. Gečevska, Application of the performance selection index method for solving machining MCDM problems, FactaUniver. Ser.: Mech. Eng. 15 (2017) 97–106 [Google Scholar]
 S. Diyaley, P. Shilal, I. Shivakoti, R.K. Ghadai, K. Kalita, PSI and TOPSIS based selection of process parameters in WEDM, Period. Polytech. Mech. Eng. 61 (2017) 255–260 [CrossRef] [Google Scholar]
 H.P. Nguyen, T.L. Banh, Q.D. Le et al., Multicriteria decision making using preferential selection index in titanium based diesinking PMEDM, J. Korean Soc. Precis. Eng. 36 (2019) 793–802 [CrossRef] [Google Scholar]
 P. Nguyen Huu, M. Thangaraj, Multi criteria decision making of vibration assisted EDM process parameters on machining silicon steel using TaguchiDEAR methodology, Silicon 13 (2021) 1879–1885 [CrossRef] [Google Scholar]
 R. Shanmugam, M.O. Ramoni, T. Geethapriyan, M. Thangaraj, Influence of additive manufactured stainless steel tool electrode on machinability of beta titanium alloy, Metals 11 (2021) 778 [CrossRef] [Google Scholar]
 B.T. Long, N.H. Phan, N. Cuong et al., Optimization of PMEDM process parameter for maximizing material removal rate by Taguchi’s method, Int. J. Adv. Manuf. Technol. 87 (2016) 1929–1939 [CrossRef] [Google Scholar]
 T. Geethapriyan, T. Muthuramalingam, K. Kalaichelvan, Influence of process parameters on machinability of Inconel 718 by electrochemical micromachining process using TOPSIS technique, Arab. J. Sci. Eng. 44 (2019) 7945–7955 [CrossRef] [Google Scholar]
 M. Thangaraj, A. Ramamurthy, K. Sridharan, S. Ashwin, Analysis of surface performance measures on WEDM processed titanium alloy with coated electrodes, Mater. Res. Express 5 (2018) 126503 [Google Scholar]
 T. Muthuramalingam, B. Mohan, Performance analysis of iso current pulse generator on machining characteristics in EDM process, Arch. Civil Mech. Eng. 14 (2014) 383–390 [CrossRef] [Google Scholar]
 T. Muthuramalingam, B. Mohan, A. Rajadurai, M.D. Antony, Experimental investigation of iso energy pulse generator on performance measures in EDM, Mater. Manufactur. Process. 28 (2013) 1137–1142 [CrossRef] [Google Scholar]
 S. Shirguppikar, M.S Patil, N.H. Phan, T. Muthuramalingam, P.V. Dong, N.C. Tam, B.T. Tai, N.D. Minh, N.V. Duc, Assessing the effects of uncoated and coated electrode on response variables in electrical discharge machining for Ti6Al4V titanium alloy, Tribol. Ind. (2021) DOI: 10.24874/ti.1020.12.20.03 [Google Scholar]
Cite this article as: Nguyen Huu Phan, Ngo Ngoc Vu, Shailesh Shirguppikar, Nguyen Trong Ly, Nguyen Chi Tam, Bui Tien Tai, Le Thi Phuong Thanh, Multicriteria decision making in electrical discharge machining with nickel coated aluminium electrode for titanium alloy using preferential selection index, Manufacturing Rev. 9, 13 (2022)
All Tables
All Figures
Fig. 1 Thin film nickel coated electrode. 

In the text 
Fig. 2 EDAX Report of nickel coating tool electrode. 

In the text 
Fig. 3 Images of electrode and workpiece. (a) Electrode surface; (b) Workpiece surface. 

In the text 
Fig. 4 Effect of I on MRR and TWR in EDM using coated electrode. 

In the text 
Fig. 5 Effect of U on MRR and TWR in EDM using coated electrode. 

In the text 
Fig. 6 Effect of T_{on} on MRR and TWR in EDM using coated electrode. 

In the text 
Fig. 7 Ranking PSI index. 

In the text 
Fig. 8 Chart of main effects for S/N ratios of θ_{j}. 

In the text 
Fig. 9 Topography of machined surface. 

In the text 
Fig. 10 Defects on the machined surface. 

In the text 
Fig. 11 White layer thickness of machined surface. 

In the text 
Current usage metrics show cumulative count of Article Views (fulltext article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 4896 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.