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Some investigations into machining of AISI D2 tool steel using wire electro discharge machining (WEDM) process
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ScienceDirect Materials Today: Proceedings 5 (2018) 24347–24357
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IConAMMA_2017
Some investigations into machining of AISI D2 tool steel using wire electro discharge machining (WEDM) process Sujeet Kumar Chaubeya*, Shankar Singhb, Abhishek Singhc a
Reserach Scholar, Discipline of Mechanical Engineering, Indian Institute of Technology Indore, Khandwa Road, Simrol- 453 552 (MP) India b Professor, Department of Mechanical Engineering, SLIET Longowal Punjab, Sangrur, Longowal - 148106 (Punjab) India c B.Tech Student, Department of Mechanical Engineering, Guru Nanak Dev Engineering College Ludhiana, (Punjab) India
Abstract This paper reports on an experimental study performed in order to optimize the wire electro discharge machining (WEDM) process parameters namely peak current (IP), pulse-on-time (Ton), wire tension (WT) and wire feed rate (WF) to maximize the material removal rate (MRR) and minimize the surface roughness (SR). This study utilizes multiple regression analysis to predict model and find the optimal parameter settings for cutting of AISI D2 tool steel by WEDM using plain brass wire of 0.25 mm diameter. Experiments were designed and planned as per Taguchi’s L9 (34) orthogonal array. Each runs were repeated twice and the mean responses have been considered. Analysis of variance (ANOVA) technique was used to determine the most important WEDM process parameters which significantly influence the material removal rate and surface roughness. It has been observed that surface roughness and material removal rate increases with increase in pulse-on-time and peak current whereas decreases with increase in wire tension and wire feed rate. Finally, it was concluded that peak current and pulse-on-time are most significant parameters which affects material removal rate and surface roughness respectively than other parameters considered in this study. Wire tension and wire feed rate have less influence on surface finish and material removal rate. Furthermore, surface roughness and material removal rate slightly decreases with increase in wire tension and wire feed rate. © 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Advances in Materials and Manufacturing Applications [IConAMMA 2017]. Keywords: WEDM; AISI D2 tool steel; Surface roughness; Material removal rate; Taguchi’s methodology; Multiple regression analysis
* Corresponding author. Tel.: +91 8959742263; fax: +91 0167. E-mail address: [email protected], [email protected] 2214-7853 © 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Advances in Materials and Manufacturing Applications [IConAMMA 2017].
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1. Introduction Rapid developments in the modern manufacturing industries such as automotive, aerospace, tool and die making, micro-manufacturing, electronics, medical, defense, robotics etc., motivate the researchers to develop and explore the advanced manufacturing processes for manufacturing of advanced materials such as tool steel, super alloys and composites. These materials have high hardness and difficult-to-machine and often exhibit several limitations in quiet and smooth machining using traditional machining processes such as milling, turning, grinding, drilling, etc. AISI D2 is a high-carbon high-chromium (HCHCr), air hardening tool steel of D-series and possess high hardness (ranging between 55-62 HRC), high wear and abrasion resistance characteristics. High chromium contents in AISI D2 steel makes it highly corrosive resistance in harden condition. It suitable for machining in annealed condition using conventional process. They are extensively used to manufacture of tools (such as punching tools) and dies (such as stamping dies, forming dies and rolls), knives, shearing blades, slitting cutters, scrap choppers, tyre shredders [1]. Manufacturing of cutting tools and dies from AISI D2 by conventional processes are very difficult due to material characteristics and limitations of conventional processes such as cutting tool should be harder than work material, frequent wear and tear of cutting tools, surface finish and dimensional accuracy, complicated machining process, requirement of special fixture and tooling arrangement and is also not suitable for fragile and complex profile. To overcome the limitations of conventional processes, researcher gave their attention on advanced manufacturing processes based on spark erosion processes such as WEDM for manufacturing of tools and dies made from AISI D2 tool steel. WEDM overcome the limitations of traditional processes of machining tool steels and meet the demands of modern machining and tooling industries. Presently, WEDM is an essential machining process for manufacturing of complex and intricated profile from difficult-to-machine materials without using any finishing process to achieve the desired surface quality of machined parts and components. Nomenclature WEDM Wire electro discharge machining AISI American Iron and Steel Institute MRR Material removal rate SR Surface roughness Ra Average surface roughness ANOVA Analysis of variance 1.1. WEDM: The process Wire electro discharge machining (WEDM) is an advanced machining process which utilizes the thermal electro mechanism to machine/cut the electrically conductive materials regardless of its hardness. In this process DC power supply are used to produce rapidly recurring electric discharge between the workpiece and wire by means of DC pulse generator. There is no contact between wire and workpiece during machining. The portion where sparks occurs is heated to highly extreme temperature (about 12000 0C), due to which work materials are melted and evaporated and then continuous flowing deionized water (also acts as a coolant) flushes away the removed particles also referred as debris from inter electrode gap (IEG). Proper flushing is essential to prevent the formation of a very hard thin layer referred as recast layer on the top of the WEDM machined surfaces. Recast layer comprises microcracks, voids, nicks and asperities which degrade the surface quality and surface integrity of WEDMed surfaces. Continuous moving fine wire and workpiece are connected to negative and positive poles of direct current (DC) power supply respectively. Both wire and workpiece electrodes should be electrically conductive. A very small gap about 0.025 mm is maintained between IEG through the servo mechanism. When DC power supplied between inter electrode gap an intense electrical field is developed at point of minimum inter-electrode gap. Microscopic contaminants suspended in deionized water are attracted towards electric field and concentrated at strongest point of electric field. These contaminants formed the highly conductive bridge across the IEG. Electrode materials and
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conductive bridge heated continuously as applying voltage increases and certain portions are ionized to build a spark channel between IEG. At this point both temperature and pressure are rapidly increased which leads to generation of sparks between IEG. The slight amount of materials melts and vaporized from the wire and workpiece at the points of sparks generation. A bubble composed of gaseous by products of vaporization rapidly expand outwards of spark channels. During the pulse-off duration, sparking and heating action stopped results in collapse of spark channels and deionized water flushes away the removed particles from IEG. The WEDM residue comprise a very small solidify ball of materials and gas bubbles. This process is continuing till complete machining of the workpiece [2-4]. Figure 1 depicts the concept of sparks generation in WEDM process.
Fig. 1. Concepts of different phases of spark generation in WEDM process.
1.2. Review of the previous research work on WEDM of tool steel and advanced materials A comprehensive literature review of previous research work has been reported on machining of tool steel (AISI D-series) and advanced materials using WEDM process and has been summarized in Tabular form as illustrated in Table A1. It is evident from the past research work that sufficient work has been performed on machining of tool steel and advanced materials using WEDM process. But very few research works have been reported on an experimental investigation into WEDM for machining of AISI D2 tool steel. Main objectives of this study are (i) to determine the effect of WEDM process parameters on MRR and SR during the machining of AISI D2 tool steel; (ii) parametric optimization of WEDM process parameters to improve the MRR and reduce the SR of WEDM machined parts; and (iii) to explore and establish WEDM as an excellent and superior alternative process for manufacturing of complicated tool and die from AISI D2 tool steel. 1.3. Experimentation 1.3.1. Materials and Methods In this study, a rectangular block of AISI D2 tool steel was selected as workpiece material having dimensions of 100 mm long; 15 mm wide; and 20 mm thick and is extensively used in tool and die manufacturing industry. D2 tool steel is high-carbon, high-chromium (HCHCr) tool steel having higher toughness, higher wear and abrasion
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resistance characteristics at elevated temperature and hardness varying in the range of 55-62 HRC. The AISI D2 block was initially prepared by milling and grinding processes to make its sides perfectly flat, parallel to opposite side and at right angle to adjacent side for its proper clamping on WEDM worktable and ensure accurate positioning with respect to wire feed. Table 1 present the chemical composition of AISI D2 tool steel determined by spectrographic analysis. Figure 2 depicts the detailed specification of AISI D2 block and specimen manufactured by WEDM process. Table 1. Presents the details of chemical composition of AISI D2 tool steel in tabular form. Constituents
C%
S%
P%
Si%
Mn%
Cr%
Mo%
V%
% wt
1.69
0.04
0.04
0.43
0.64
11.30
0.59
0.2
Fig. 2. Specifications of work specimen AISI D2 tool steel block and WEDMed specimen.
1.3.2. Experimental procedure The experiments were performed on 4 axes (X, Y, U and V) computerized numerical control (CNC) WEDM machine (model: Sprintcut 734 from Electronica India Ltd. Pune, India) for machining of AISI D2 tool steel using plain brass wire of 0.25 mm diameter (having tensile strength of 450-490 N/m2) as tool electrode and deionized water (having conductivity of 20 Ω) as a dielectric fluid.
Fig. 3. Sequences of activities involve in machining of AISI D2 tool steel by WEDM process.
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Usually, WEDM consist four components namely positioning system, power supply system, wire drive system and dielectric supply system. Fresh wire is continuously fed through supply spool which travels through the work piece and is kept in tension with the help of upper and lower wire guides. In this process the sparks are generated between continuously travelling brass wire and AISI D2 tool steel (at IEG). This machine can cut the material at an inclination angle up to ± 300 having height up to 50 mm. In this study, specimen (having size of 10 mm long; 15 mm wide; and 20 mm thick) was cut from AISI D2 block using brass wire travelling along cross section of the AISI D2 block. Total 18 specimens were machined from AISI D2 blocks by WEDM process using different machining condition. Figure 3 depicts sequences of various activity involve during machining of AISI D2 tool steel by WEDM process. 1.3.3. Methodology The experimental design based on Taguchi’s orthogonal array technique would significantly decrease the experimental runs. The experimental runs were designed and planned using Taguchi’s L9 (34) orthogonal array having 8 degrees of freedom and consist four columns and 9 rows. Therefore, four WEDM input parameters (having three levels each) can be assigned to the four columns and nine rows to conduct 9 experiments using different WEDM parametric settings of pulse-on time, peak current, wire feed rate and wire tension. Each experimental runs were replicate twice and the average values of MRR and average surface roughness (Ra) of each experimental runs were considered. Levels and values of WEDM input parameters were selected on the basis of trial runs by making 23 straight cut of 2 mm on AISI D2 block keeping the view of wire breakage and cutting rate. Variation in MRR and Ra with WEDM process parameters was analyzed mathematically using the regression analysis. The levels and values of variable and fixed input parameters and selected responses for experiments are presented in Table 2. Table 2. Presents the details of variable and fixed process parameters and responses used in the experiments. Variable WEDM process parameters, Levels Symbol (units) I II Peak current ‘IP’ (A) 60 120 Pulse-on-time ‘Ton’ (µs) 0.5 1.6 Wire feed rate ‘WF’ (m/min) 5 7 Wire tension ‘WT’ (g) 900 1140
Responses and fixed parameters III 180 2.6 9 1380
Responses: Material removal rate (MRR); average surface roughness (Ra) WEDM fixed parameters: 37.5 μs pulse-off-time; 20 Volts servo-gap voltage; 15 kg/cm2 dielectric pressure; 2100 servo-gap voltage, 100% cutting speed and 20 Ω Dielectric conductivity
1.3.4. Measurement of responses In this study MRR and Ra were considered as a response parameters. Cutting/Machining speed (Cs) and machining time was shown on the monitor of the WEDM during machining. Machining time can be also calculated by a stop watch having a least count of 0.01 seconds. Material removal rate was mathematically expressed as follow; (
)=
×
(
)=
×
(
)=
.
× ℎ .
---------------- (1)
[14]
------- (2)
[15]
---------------- (3)
[16]
.
Where Cs stand for machining speed in mm/min; L stand for width of cut; b stand for width of cut; h stand for height; Wi stand for weight of gear plate before WEDM in gram; Wf stand for weight of gear plate after WEDM in gram, t for machining time in minute; ρ stand for material density in g/mm3. Equation 1 was chosen for calculating MRR. Mitutoyo surftest portable device (model: SJ- 301 from Mitutoyo, Japan) was used to measure the values of average surface roughness (Ra) by tracing probe having diamond on WEDM surfaces across the direction of wire path for an evaluation length of 4 mm, cut-off length of 0.8 mm and Gaussian filter to differentiate between waviness and roughness profiles. Measurements of surface roughness were conducted at the five different points and
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consider the average of these five measurements. Microstructure of the best finish D2 tool steel specimen was achieved by SUPRA 55 field emission scanning electron microscope (FE-SEM) from Carl Zeiss, Germany. 1.4. Results and discussions Table A2 presents the values of WEDM process parameters and responses after machining of AISI D2 tool steel for all experimental runs. The following section presents the analysis of effects of four WEDM process parameters on the responses and corresponding regression plots with experimental data points. 1.4.1. Effect of WEDM process parameters on material removal rate (MRR) and average surface roughness (Ra) Figure 4a-4d graphically represent the main effect plots indicating the influence of four WEDM process parameters on MRR and Ra during machining of AISI D2 tool steel by WEDM process and showing experiment data points, and best fit curves to these data points (red colour for MRR and blue colour for Ra) attained by regression analysis and values of WEDM parameters kept constant in each graph.
(a)
(c)
(b)
(d)
Fig. 4. Variation in MRR and Ra with WEDM process parameters during machining of AISI D2 tool steel: (a) peak current; (b) pulse-on-time; (c) wire feed rate; and (d) wire tension.
It can be seen in Fig. 4a that MRR and Ra increase non-linearly with increase in peak current. This can be clarified by the fact that at increase values of peak current causes generation of high discharge energy of sparks in IEG which generates violent sparks and sparks forces resulted in formation of irregular shape deeper craters on top
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of the WEDM machined surface. Finally, these cause higher MRR and higher average surface roughness of AISI D2 tool steel [17-18]. Fig. 4b depicts that MRR increase linearly whereas Ra increases non-linearly with increase in pulse-on-time. This is due to fact that higher values of pulse-on-time produce longer duration of sparks in IEG thus more discharge energy of a sparks conveyed to the wire and the AISI D2 block. This cause generation of violent sparks and spark forces (gas bubbles) resulted in formation of irregular shape deeper craters on WEDMed surfaces, wire deflection, melting and deposition of wire particles on top surfaces of workpiece increase the MRR but deteriorating their surface finish [18-21]. It can be observed from Fig. 4c that Ra increases non-linearly to a certain value then start decreasing non-linearly with increase in wire tension whereas MRR slightly decreases non-linearly with increase in wire tension. Lower wire tension will cause more wire vibration which increase surface roughness [18]. Fig. 4d illustrates that MRR decreases linearly whereas Ra decreases non-linearly with increase in wire feed rate. Firstly, Ra slightly decreases non-linearly to a certain value then start decreasing nearly linearly. At higher wire feed rate, fresh wire always comes to contact of spark for machining which significantly reduce the concentration of sparks at a certain place on the wire which significantly decreases wire breakage, wire lag and machining streaks [17]. Thus reduce the surface roughness of the AISI D2 tool steel machined by WEDM process. Based on the above observations it has been concluded that higher material removal rate can be achieved at optimum parameters having 240 A peak current, 2.6 µs pulse-on-time, 5 m/min. wire feed rate and 900g wire tension. Whereas, better surface finish can be achieved at optimum parameters having 60 A peak current, 0.6 µs pulse-on-time, 1380g wire tension and 9 m/min. wire feed rate. It can be observed from Table A2 that minimum surface roughness is achieved in experimental run 7 having parametric combination of 180 A peak current, 0.6 µs pulse-on-time, 1380g wire tension and 7 m/min wire feed. Whereas, higher material removal rate is achieved in experimental run 9 having parametric combination of 180 A peak current, 2.6 µs pulse-on-time, 1140g wire tension and 5 m/min. wire feed. 1.4.2. Regression analysis of material removal rate Table 3 presents the regression analysis for material removal rate (MRR). Regression equation of fitted model for material removal rate is given in equation 4. It can be seen from Table 3 that pulse-on-time, peak current and wire feed rate are significant WEDM process parameters at the confidence interval of 95%. Therefore, final model can be expressed as given in equation 5. Table 4 presents the analysis of variance (ANOVA) for MRR to determine the significance of regression model. P value of regression model is 0.003 which is less than 0.05. Hence regression model for MRR is significant. Table 3. Multiple regression analysis for material removal rate ‘MRR’ (mm2/min). Predictor Constant Ip (A) Ton (µs) WT (g) WF (m/min)
Coef. -101.19 0.09250 1.1375 -1.3692 -1.6933
SE Coef. 15.33 0.02015 0.1209 0.6044 0.6044
T -6.51 4.59 9.41 -2.27 -2.80
P 0.003 0.010 0.001 0.086 0.049
Remarks Significant Significant Significant Not significant Significant
Therefore fitted model is expressed by the following equation: ( / ) = −101 + 0.0925 + 1.14 − 1.37 − 1.69 … … … … … … (4) Since WT is not significant at 95% confidence interval, it can be removed from equation 4. Thus, the final model can be express as follows; ( / ) = −101 + 0.0925 + 1.14 − 1.69 … … … … … … (5) Table 4. ANOVA for material removal rate ‘MRR’. Source Model Residual Total
Degree of freedom 4 4 8
Sum of Squares 1074.97 35.07 1110.03
Mean Square
F-Ratio
P-Value
268.74 8.77
30.66
0.003
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1.4.3. Regression analysis of average surface roughness Table 5 presents the regression analysis for average surface roughness (Ra). Regression equation of fitted model for average surface roughness is given in equation 6. It can be seen from Table 5 that pulse-on time is most significant parameters at the confidence interval of 95%. Therefore, final model can be express as given in equation 7. Table 6 presents the ANOVA for Ra to determine the significance of regression model. P value of regression model is 0.015 which is less than 0.05. Hence regression model for Ra is significant. Table 5. Multiple regression analysis for average surface roughness (Ra). Predictor Constant Ip (A) Ton (µs) WT (g) WF (m/min)
Coef. -8.046 0.001899 0.09544 -0.03067 -0.03966
SE Coef. 1.728 0.002240 0.01344 0.06721 0.06721
T -4.66 0.89 7.10 -0.46 -0.59
P 0.010 0.467 0.002 0.672 0.587
Remarks Significant Not significant Significant Not significant Not significant
Therefore fitted model is expressed by the following equation: = −8.05 + 0.0954 − 0.0307 − 0.0397 … … … … . . (6) Since IP, WT and WF are not significant at 95% confidence interval, it can be removed from equation 6. Thus, the final model can be express as follows; = −8.05 + 0.0954 … … … … . . (7) Table 6. ANOVA for average surface roughness (Ra) Source Model Residual Total
Degree of freedom 4 4 8
Sum of squares Mean square 5.5950 1.3987 0.4337 0.1084 6.0286
F-Ratio 12.90
P-Value 0.015
1.5. Microstructure of the best finish WEDMed AISI D2 tool steel specimen Figure 5 depict the microstructure of the best finish WEDMed specimen (AISI D2 tool steel). Microstructure of the best finish WEDMed surface of AISI D2 tool steel revealed that WEDMed surface are free from burr, microcracks, nicks, asperities and voids.
Fig. 5. SEM micrograph of the best finish AISI D2 specimen machined by WEDM process.
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Conclusions This study described the experimental investigations aimed to know the variations in performance measures (i.e. MRR and Ra) with WEDM process parameters, parametric optimization of variable WEDM process parameters to maximize the material removal rate, analysis of variance (ANOVA) of MRR and Ra has been used to determine the most influencing WEDM process parameters and significant of the model on the basis of % values of P test and regression analysis to predict the MRR and Ra during machining of AISI D2 tool steel. Peak current, pulse-on-time, wire tension and wire feed rate were considered as variable process parameters having three levels each. Experiments were conducted using Taguchi L9 (34) orthogonal array. Peak current, pulse-on-time, wire tension and wire feed rate were selected as variable WEDM process parameters having three levels each to know the behaviour of performance measures namely MRR and Ra. Following conclusion can be drawn from this study: Irregular shape deeper craters, violent sparks, wire vibration and improper flushing are main factors which affect material removal rate and surface roughness. Peak current, pulse-on-time and wire feed rate have more influence on material removal rate of AISI D2 tool steel than other WEDM process parameters. Pulse-on-time has more influence on surface roughness of AISI D2 tool steel than other WEDM process parameters. Higher MRR can be achieved using higher peak current, higher pulse-on-time, lower wire feed and lower wire tension. Minimum surface roughness can be achieved using lower peak current, lower pulse-on time, higher wire feed and higher wire tension. Higher material removal rate can be achieved using 240 A peak current, 2.6 µs pulse-on-time, 900g wire tension and 5 m/min wire feed rate. Minimum surface roughness can be achieved using 60 A peak current, 0.6 µs pulse-on-time, 9 m/min wire feed rate and 1380g wire tension as optimal parameters. Acknowledgements Authors express their sincere gratitude to technical staff of Central Workshop SLIET Longowal (Punjab) for their support during preparation of workpiece (AISI D2 tool steel) and Mr. Ram Mehar for his support and help to perform the experimental runs on Electronica Sprintcut 734 WEDM machine at Shriram Industry, Ghaziabad. References [1] http://www.steelexpress.co.uk/toolsteel/D2-Steel-properties.html [2] Y. Pachaury, P. Tandon, An overview of electric discharge machining of ceramics and ceramic based composites, J. of Manuf. Proc. 25 (2017) 369–390. [3] K.H. Ho, S.T. Newman, S. Rahimifard, R.D. Allen, State of the art in wire electrical discharge machining (WEDM), International J. of Mach. Tools and Manuf. 44 (12-13) (2004) 1247–1259. [4] B.A. Schumacher, After 60 years of EDM the discharge process remains still disputed, J. of Mat. Proc. Tech. 149 (2004) 376-381. [5] A. Hasçalýk, U. Çaydas, Experimental study of wire electrical discharge machining of AISI D5 tool steel, J. of Mat. Proc. Technol. 148 (2004) 362–367. [6] S. Sarkar, S. Mitra, B. Bhattacharyya, Parametric analysis and optimization of wire electrical discharge machining of γ-titanium aluminide alloy, J. of Mat. Proc. Technol. 159 (2005) 286–294. [7] S.S. Mahapatra, A. Patnaik, Optimization of wire electrical discharge machining (WEDM) process parameters using Taguchi method, Int. J. Adv. Manuf. Technol, 34 (2007) 911-925. [8] K. Kanlayasiri, S. Boonmung, An investigation on effects of wire-EDM machining parameters on surface roughness of newly developed DC53 die steel, J. of Mat. Proc. Technol. 187–188 (2007) 26–29. [9] A. Ikram, N.A. Mufti, M.Q. Saleem, A.R. Khan, Parametric optimization for surface roughness, kerf and MRR in wire electrical discharge machining (WEDM) using Taguchi design of experiment, J. Mech. Sci. Technol. 27(7) (2013) 2133-2141, http://dx.doi.org/10.1007/s12206-013-0526-8. [10] V. Singh, S.K. Pradhan, Optimization of WEDM parameters using Taguchi technique and Response Surface Methodology in machining of AISI D2 Steel, Proc. Eng. 97 ( 2014 ) 1597 – 1608.
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[11] N. Sharma, A. Singh, R. Sharma, Deepak, Modelling the WEDM Process Parameters for Cryogenic Treated D-2 Tool Steel by integrated RSM and GA, Proc. Eng. 97 ( 2014 ) 1609 – 1617. [12] B. K. Lodhi, S. Agarwal, Optimization of machining parameters in WEDM of AISI D3 Steel using Taguchi Technique, Proc. CIRP 14 (2014) 194 – 199. [13] V. Singh, R. Bhandari1, V.K. Yadav, An experimental investigation on machining parameters of AISI D2 steel using WEDM, Int J Adv Manuf Technol, 2016, DOI 10.1007/s00170-016-8681-6. [14] R. Ramakrishnan, L. Karunamoorthy, Multi response optimization of wire EDM operations using robust design of experiments, Int. J. of Adv. Manuf. Technol. 29 (2006) 105–112. [15] P.S. Rao, K. Ramji, B. Satyanarayana, Effect of WEDM conditions on surface roughness: A parametric optimization using Taguchi method, Int. J. of Adv. Eng. Sci. and Technol. 6 (1) (2011) 041– 048. [16] S. Balasubramanian, S. Ganapathy, Grey relational analysis to determine optimum process parameters for Wire Electro Discharge Machining (WEDM), Int. J.l of Eng. Sci. and Technol. 3 (1) (2011) 95-101. [17] K.H. Ho, S.T. Newman, S. Rahimifard, R.D. Allen, State-of-the-art in wire electrical discharge machining, Int. J of Mach. Tools and Manuf. 44 (2004) 1247–1259. [18] P.H. Yu, H.K. Lee, Y.X. Lin, S.J. Qin, B.H. Yan, F.Y. Huang, Machining characteristics of polycrystalline silicon by wire electrical discharge machining, Mat. and Manuf. Proc. 26 (2011) 1443–1450. [19] A.B. Puri, B. Bhattacharyya, Modeling and analysis of the wire-tool vibration in wire-cut EDM, J of Mat. Proc. Technol. 141 (2003) 295–301. [20] Y.S. Liao, J.T. Huang, Y.H. Chen, A study to achieve fine surface finish in Wire-EDM, J of Materials Processing Technol. 149 (2004) 165–171. [21] C. Arunachalam, M. Aulia, B. Bozkurt, P.T. Eubank, Wire vibration, bowing, and breakage in wire electrical discharge machining, J of Appl. Phy. 89 (8) (2001) 4255–4262.
Appendix A. Table A1. Summary of past research works on machining of tool steel (D-series) materials using WEDM process. Authors
Workpiece and tool materials Hasçalýk et AISI D5 tool al. (2004) [5] steel
Sarkar et al. (2005) [6]
Mahapatra et al. (2007) [7]
Kanlayasiri and Boonmung (2007) [8]
Methodology
Process parameters and Responses Open circuit voltage, pulse duration, wire feed rate, dielectric pressure Surface roughness, Micro-structure
Findings
Surface roughness and micro-cracks increase with increase in pulse duration and open circuit voltage. CuZn37 brass Cracks penetration into the HAZ wire of 0.25 mm depending on pulse energy diameter WEDMed surface is harder than core material due to formation of white layer Taguchi’s L18 γ-titanium Pulse-on-time, pulse-off-time, Machining rate, surface roughness aluminide alloy Orthogonal Array peak current, servo-gap voltage, and dimensional deviation increases Additive model wire tension, dielectric pressure with increase in pulse-on-time and Brass wire of peak current whereas cutting speed 0.25mm Cutting speed, surface decrease with increase in pulse-offdiameter roughness, time dimensional deviation Surface roughness and dimensional deviation are independent of the pulse-off time. Taguchi's L27 Discharge current, pulse-onAISI D2 tool Discharge current, pulse-on-time, and time, steel orthogonal dielectric flow rate and their array and Non-linear pulse frequency, wire feed, interactions are most significant wire tension, dielectric flow rate parameters Stratified wire regression Material removal rate, (zinc coated analysis Optimized process parameters can be surface roughness, kerf width copper wire) of used for maximizing the MRR and 0.25-mm minimizing of SR and kerf width diameter DC53 die steel Full factorial Pulse-on-time, pulse-off-time, Pulse-on-time and pulse-peak current design (2k) peak current, Wire tension are most significant that affect Cu–35 wt%Zn surface roughness of DC53 die steel wire of 0.25 Surface roughness machined by WEDM mm dia. Factorial design
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Table A1. Continue… Authors Ikram et al. (2013) [9]
Workpiece and tool materials AISI D2 tool steel Brass wire of 0.25mm diameter
Singh et al. (2014) [10]
AISI D2 tool steel Brass wire of 0.25 mm diameter
Sharma et al. Cryogenic (2014) [11] treated AISI D2 tool steel CuZn37 brass wire of 0.25 mm dia. Lodhi and AISI D3 steel Agarwal Zinc coated (2014) [12] brass wire of 0.25 mm dia. Singh et al. AISI D2 tool (2016) [13] steel Brass wire of 0.25 mm diameter
Methodology
Process parameters and Findings Responses Taguchi's L18 Pulse-on-time, pulse-off-time, MRR increases and surface finish orthogonal array open voltage, wire feed rate, decreases with increase in pulse-on wire tension, dielectric pressure, time, open voltage. MRR decreases Regression analysis servo-gap voltage, with increase in servo voltage Material removal rate, Kerf increases with increase in open surface roughness, kerf width voltage, pulse-on time and wire tension. Taguchi’s L27 (34) Pulse-on-time, pulse-off-time Pulse-on time and pulse-off time are orthogonal array servo-gap voltage, wire feed most significant parameters that Response surface rate affect the MRR methodology (RSM) Pulse-on time and servo-gap voltage Material removal rate, significantly affect the surface surface roughness roughness Central composite rotatable design (CCRD) and RSM
Pulse on time, pulse-off time servo-gap voltage, peak current Surface roughness
It was observed that slope of SR curve declined with increase in peak current Surface roughness increases with increase in pulse-on-time and servogap voltage Pulse-on-time and peak current are most significant factors than other parameters.
Taguchi’s L9 Orthogonal Array
Pulse-on time, pulse-off time peak current, wire feed rate Surface roughness
Taguchi’s L27 (35) orthogonal array Response surface methodology (RSM)
Pulse-on-time, pulse-off-time, Pulse-on-time, pulse-off-time, and peak current, servo-gap servo-gap voltage are most significant voltage, parameters that affect cutting rate and wire feed rate MRR Material removal rate, Pulse-on-time and servo-gap voltage surface roughness are significantly affect the surface roughness whereas servo voltage significantly affect the gap voltage
Table A2. Values of variable WEDM process parameters and responses for experiments. WEDM process parameters
Responses
Exp.
IP
Ton
WF
WT
MRR
Ra
Runs
(A)
(µs)
(m/min)
(g)
(mm2/min)
(μm)
1
60
0.6
5
900
5.1
1.47
2
60
0.6
7
1140
12.7
2.88
3
60
2.6
9
1380
15.6
3.10
4
120
0.6
9
900
6.75
1.55
5
120
1.6
5
1380
20.6
2.95
6
120
2.6
7
900
33.9
3.55
7
180
0.6
7
1380
7.5
1.54
8
180
1.6
9
900
21.1
2.93
9
180
2.6
5
1140
38.1
3.64
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ScienceDirect Materials Today: Proceedings 5 (2018) 24347–24357
www.materialstoday.com/proceedings
IConAMMA_2017
Some investigations into machining of AISI D2 tool steel using wire electro discharge machining (WEDM) process Sujeet Kumar Chaubeya*, Shankar Singhb, Abhishek Singhc a
Reserach Scholar, Discipline of Mechanical Engineering, Indian Institute of Technology Indore, Khandwa Road, Simrol- 453 552 (MP) India b Professor, Department of Mechanical Engineering, SLIET Longowal Punjab, Sangrur, Longowal - 148106 (Punjab) India c B.Tech Student, Department of Mechanical Engineering, Guru Nanak Dev Engineering College Ludhiana, (Punjab) India
Abstract This paper reports on an experimental study performed in order to optimize the wire electro discharge machining (WEDM) process parameters namely peak current (IP), pulse-on-time (Ton), wire tension (WT) and wire feed rate (WF) to maximize the material removal rate (MRR) and minimize the surface roughness (SR). This study utilizes multiple regression analysis to predict model and find the optimal parameter settings for cutting of AISI D2 tool steel by WEDM using plain brass wire of 0.25 mm diameter. Experiments were designed and planned as per Taguchi’s L9 (34) orthogonal array. Each runs were repeated twice and the mean responses have been considered. Analysis of variance (ANOVA) technique was used to determine the most important WEDM process parameters which significantly influence the material removal rate and surface roughness. It has been observed that surface roughness and material removal rate increases with increase in pulse-on-time and peak current whereas decreases with increase in wire tension and wire feed rate. Finally, it was concluded that peak current and pulse-on-time are most significant parameters which affects material removal rate and surface roughness respectively than other parameters considered in this study. Wire tension and wire feed rate have less influence on surface finish and material removal rate. Furthermore, surface roughness and material removal rate slightly decreases with increase in wire tension and wire feed rate. © 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Advances in Materials and Manufacturing Applications [IConAMMA 2017]. Keywords: WEDM; AISI D2 tool steel; Surface roughness; Material removal rate; Taguchi’s methodology; Multiple regression analysis
* Corresponding author. Tel.: +91 8959742263; fax: +91 0167. E-mail address: [email protected], [email protected] 2214-7853 © 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Advances in Materials and Manufacturing Applications [IConAMMA 2017].
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1. Introduction Rapid developments in the modern manufacturing industries such as automotive, aerospace, tool and die making, micro-manufacturing, electronics, medical, defense, robotics etc., motivate the researchers to develop and explore the advanced manufacturing processes for manufacturing of advanced materials such as tool steel, super alloys and composites. These materials have high hardness and difficult-to-machine and often exhibit several limitations in quiet and smooth machining using traditional machining processes such as milling, turning, grinding, drilling, etc. AISI D2 is a high-carbon high-chromium (HCHCr), air hardening tool steel of D-series and possess high hardness (ranging between 55-62 HRC), high wear and abrasion resistance characteristics. High chromium contents in AISI D2 steel makes it highly corrosive resistance in harden condition. It suitable for machining in annealed condition using conventional process. They are extensively used to manufacture of tools (such as punching tools) and dies (such as stamping dies, forming dies and rolls), knives, shearing blades, slitting cutters, scrap choppers, tyre shredders [1]. Manufacturing of cutting tools and dies from AISI D2 by conventional processes are very difficult due to material characteristics and limitations of conventional processes such as cutting tool should be harder than work material, frequent wear and tear of cutting tools, surface finish and dimensional accuracy, complicated machining process, requirement of special fixture and tooling arrangement and is also not suitable for fragile and complex profile. To overcome the limitations of conventional processes, researcher gave their attention on advanced manufacturing processes based on spark erosion processes such as WEDM for manufacturing of tools and dies made from AISI D2 tool steel. WEDM overcome the limitations of traditional processes of machining tool steels and meet the demands of modern machining and tooling industries. Presently, WEDM is an essential machining process for manufacturing of complex and intricated profile from difficult-to-machine materials without using any finishing process to achieve the desired surface quality of machined parts and components. Nomenclature WEDM Wire electro discharge machining AISI American Iron and Steel Institute MRR Material removal rate SR Surface roughness Ra Average surface roughness ANOVA Analysis of variance 1.1. WEDM: The process Wire electro discharge machining (WEDM) is an advanced machining process which utilizes the thermal electro mechanism to machine/cut the electrically conductive materials regardless of its hardness. In this process DC power supply are used to produce rapidly recurring electric discharge between the workpiece and wire by means of DC pulse generator. There is no contact between wire and workpiece during machining. The portion where sparks occurs is heated to highly extreme temperature (about 12000 0C), due to which work materials are melted and evaporated and then continuous flowing deionized water (also acts as a coolant) flushes away the removed particles also referred as debris from inter electrode gap (IEG). Proper flushing is essential to prevent the formation of a very hard thin layer referred as recast layer on the top of the WEDM machined surfaces. Recast layer comprises microcracks, voids, nicks and asperities which degrade the surface quality and surface integrity of WEDMed surfaces. Continuous moving fine wire and workpiece are connected to negative and positive poles of direct current (DC) power supply respectively. Both wire and workpiece electrodes should be electrically conductive. A very small gap about 0.025 mm is maintained between IEG through the servo mechanism. When DC power supplied between inter electrode gap an intense electrical field is developed at point of minimum inter-electrode gap. Microscopic contaminants suspended in deionized water are attracted towards electric field and concentrated at strongest point of electric field. These contaminants formed the highly conductive bridge across the IEG. Electrode materials and
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conductive bridge heated continuously as applying voltage increases and certain portions are ionized to build a spark channel between IEG. At this point both temperature and pressure are rapidly increased which leads to generation of sparks between IEG. The slight amount of materials melts and vaporized from the wire and workpiece at the points of sparks generation. A bubble composed of gaseous by products of vaporization rapidly expand outwards of spark channels. During the pulse-off duration, sparking and heating action stopped results in collapse of spark channels and deionized water flushes away the removed particles from IEG. The WEDM residue comprise a very small solidify ball of materials and gas bubbles. This process is continuing till complete machining of the workpiece [2-4]. Figure 1 depicts the concept of sparks generation in WEDM process.
Fig. 1. Concepts of different phases of spark generation in WEDM process.
1.2. Review of the previous research work on WEDM of tool steel and advanced materials A comprehensive literature review of previous research work has been reported on machining of tool steel (AISI D-series) and advanced materials using WEDM process and has been summarized in Tabular form as illustrated in Table A1. It is evident from the past research work that sufficient work has been performed on machining of tool steel and advanced materials using WEDM process. But very few research works have been reported on an experimental investigation into WEDM for machining of AISI D2 tool steel. Main objectives of this study are (i) to determine the effect of WEDM process parameters on MRR and SR during the machining of AISI D2 tool steel; (ii) parametric optimization of WEDM process parameters to improve the MRR and reduce the SR of WEDM machined parts; and (iii) to explore and establish WEDM as an excellent and superior alternative process for manufacturing of complicated tool and die from AISI D2 tool steel. 1.3. Experimentation 1.3.1. Materials and Methods In this study, a rectangular block of AISI D2 tool steel was selected as workpiece material having dimensions of 100 mm long; 15 mm wide; and 20 mm thick and is extensively used in tool and die manufacturing industry. D2 tool steel is high-carbon, high-chromium (HCHCr) tool steel having higher toughness, higher wear and abrasion
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resistance characteristics at elevated temperature and hardness varying in the range of 55-62 HRC. The AISI D2 block was initially prepared by milling and grinding processes to make its sides perfectly flat, parallel to opposite side and at right angle to adjacent side for its proper clamping on WEDM worktable and ensure accurate positioning with respect to wire feed. Table 1 present the chemical composition of AISI D2 tool steel determined by spectrographic analysis. Figure 2 depicts the detailed specification of AISI D2 block and specimen manufactured by WEDM process. Table 1. Presents the details of chemical composition of AISI D2 tool steel in tabular form. Constituents
C%
S%
P%
Si%
Mn%
Cr%
Mo%
V%
% wt
1.69
0.04
0.04
0.43
0.64
11.30
0.59
0.2
Fig. 2. Specifications of work specimen AISI D2 tool steel block and WEDMed specimen.
1.3.2. Experimental procedure The experiments were performed on 4 axes (X, Y, U and V) computerized numerical control (CNC) WEDM machine (model: Sprintcut 734 from Electronica India Ltd. Pune, India) for machining of AISI D2 tool steel using plain brass wire of 0.25 mm diameter (having tensile strength of 450-490 N/m2) as tool electrode and deionized water (having conductivity of 20 Ω) as a dielectric fluid.
Fig. 3. Sequences of activities involve in machining of AISI D2 tool steel by WEDM process.
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Usually, WEDM consist four components namely positioning system, power supply system, wire drive system and dielectric supply system. Fresh wire is continuously fed through supply spool which travels through the work piece and is kept in tension with the help of upper and lower wire guides. In this process the sparks are generated between continuously travelling brass wire and AISI D2 tool steel (at IEG). This machine can cut the material at an inclination angle up to ± 300 having height up to 50 mm. In this study, specimen (having size of 10 mm long; 15 mm wide; and 20 mm thick) was cut from AISI D2 block using brass wire travelling along cross section of the AISI D2 block. Total 18 specimens were machined from AISI D2 blocks by WEDM process using different machining condition. Figure 3 depicts sequences of various activity involve during machining of AISI D2 tool steel by WEDM process. 1.3.3. Methodology The experimental design based on Taguchi’s orthogonal array technique would significantly decrease the experimental runs. The experimental runs were designed and planned using Taguchi’s L9 (34) orthogonal array having 8 degrees of freedom and consist four columns and 9 rows. Therefore, four WEDM input parameters (having three levels each) can be assigned to the four columns and nine rows to conduct 9 experiments using different WEDM parametric settings of pulse-on time, peak current, wire feed rate and wire tension. Each experimental runs were replicate twice and the average values of MRR and average surface roughness (Ra) of each experimental runs were considered. Levels and values of WEDM input parameters were selected on the basis of trial runs by making 23 straight cut of 2 mm on AISI D2 block keeping the view of wire breakage and cutting rate. Variation in MRR and Ra with WEDM process parameters was analyzed mathematically using the regression analysis. The levels and values of variable and fixed input parameters and selected responses for experiments are presented in Table 2. Table 2. Presents the details of variable and fixed process parameters and responses used in the experiments. Variable WEDM process parameters, Levels Symbol (units) I II Peak current ‘IP’ (A) 60 120 Pulse-on-time ‘Ton’ (µs) 0.5 1.6 Wire feed rate ‘WF’ (m/min) 5 7 Wire tension ‘WT’ (g) 900 1140
Responses and fixed parameters III 180 2.6 9 1380
Responses: Material removal rate (MRR); average surface roughness (Ra) WEDM fixed parameters: 37.5 μs pulse-off-time; 20 Volts servo-gap voltage; 15 kg/cm2 dielectric pressure; 2100 servo-gap voltage, 100% cutting speed and 20 Ω Dielectric conductivity
1.3.4. Measurement of responses In this study MRR and Ra were considered as a response parameters. Cutting/Machining speed (Cs) and machining time was shown on the monitor of the WEDM during machining. Machining time can be also calculated by a stop watch having a least count of 0.01 seconds. Material removal rate was mathematically expressed as follow; (
)=
×
(
)=
×
(
)=
.
× ℎ .
---------------- (1)
[14]
------- (2)
[15]
---------------- (3)
[16]
.
Where Cs stand for machining speed in mm/min; L stand for width of cut; b stand for width of cut; h stand for height; Wi stand for weight of gear plate before WEDM in gram; Wf stand for weight of gear plate after WEDM in gram, t for machining time in minute; ρ stand for material density in g/mm3. Equation 1 was chosen for calculating MRR. Mitutoyo surftest portable device (model: SJ- 301 from Mitutoyo, Japan) was used to measure the values of average surface roughness (Ra) by tracing probe having diamond on WEDM surfaces across the direction of wire path for an evaluation length of 4 mm, cut-off length of 0.8 mm and Gaussian filter to differentiate between waviness and roughness profiles. Measurements of surface roughness were conducted at the five different points and
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consider the average of these five measurements. Microstructure of the best finish D2 tool steel specimen was achieved by SUPRA 55 field emission scanning electron microscope (FE-SEM) from Carl Zeiss, Germany. 1.4. Results and discussions Table A2 presents the values of WEDM process parameters and responses after machining of AISI D2 tool steel for all experimental runs. The following section presents the analysis of effects of four WEDM process parameters on the responses and corresponding regression plots with experimental data points. 1.4.1. Effect of WEDM process parameters on material removal rate (MRR) and average surface roughness (Ra) Figure 4a-4d graphically represent the main effect plots indicating the influence of four WEDM process parameters on MRR and Ra during machining of AISI D2 tool steel by WEDM process and showing experiment data points, and best fit curves to these data points (red colour for MRR and blue colour for Ra) attained by regression analysis and values of WEDM parameters kept constant in each graph.
(a)
(c)
(b)
(d)
Fig. 4. Variation in MRR and Ra with WEDM process parameters during machining of AISI D2 tool steel: (a) peak current; (b) pulse-on-time; (c) wire feed rate; and (d) wire tension.
It can be seen in Fig. 4a that MRR and Ra increase non-linearly with increase in peak current. This can be clarified by the fact that at increase values of peak current causes generation of high discharge energy of sparks in IEG which generates violent sparks and sparks forces resulted in formation of irregular shape deeper craters on top
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of the WEDM machined surface. Finally, these cause higher MRR and higher average surface roughness of AISI D2 tool steel [17-18]. Fig. 4b depicts that MRR increase linearly whereas Ra increases non-linearly with increase in pulse-on-time. This is due to fact that higher values of pulse-on-time produce longer duration of sparks in IEG thus more discharge energy of a sparks conveyed to the wire and the AISI D2 block. This cause generation of violent sparks and spark forces (gas bubbles) resulted in formation of irregular shape deeper craters on WEDMed surfaces, wire deflection, melting and deposition of wire particles on top surfaces of workpiece increase the MRR but deteriorating their surface finish [18-21]. It can be observed from Fig. 4c that Ra increases non-linearly to a certain value then start decreasing non-linearly with increase in wire tension whereas MRR slightly decreases non-linearly with increase in wire tension. Lower wire tension will cause more wire vibration which increase surface roughness [18]. Fig. 4d illustrates that MRR decreases linearly whereas Ra decreases non-linearly with increase in wire feed rate. Firstly, Ra slightly decreases non-linearly to a certain value then start decreasing nearly linearly. At higher wire feed rate, fresh wire always comes to contact of spark for machining which significantly reduce the concentration of sparks at a certain place on the wire which significantly decreases wire breakage, wire lag and machining streaks [17]. Thus reduce the surface roughness of the AISI D2 tool steel machined by WEDM process. Based on the above observations it has been concluded that higher material removal rate can be achieved at optimum parameters having 240 A peak current, 2.6 µs pulse-on-time, 5 m/min. wire feed rate and 900g wire tension. Whereas, better surface finish can be achieved at optimum parameters having 60 A peak current, 0.6 µs pulse-on-time, 1380g wire tension and 9 m/min. wire feed rate. It can be observed from Table A2 that minimum surface roughness is achieved in experimental run 7 having parametric combination of 180 A peak current, 0.6 µs pulse-on-time, 1380g wire tension and 7 m/min wire feed. Whereas, higher material removal rate is achieved in experimental run 9 having parametric combination of 180 A peak current, 2.6 µs pulse-on-time, 1140g wire tension and 5 m/min. wire feed. 1.4.2. Regression analysis of material removal rate Table 3 presents the regression analysis for material removal rate (MRR). Regression equation of fitted model for material removal rate is given in equation 4. It can be seen from Table 3 that pulse-on-time, peak current and wire feed rate are significant WEDM process parameters at the confidence interval of 95%. Therefore, final model can be expressed as given in equation 5. Table 4 presents the analysis of variance (ANOVA) for MRR to determine the significance of regression model. P value of regression model is 0.003 which is less than 0.05. Hence regression model for MRR is significant. Table 3. Multiple regression analysis for material removal rate ‘MRR’ (mm2/min). Predictor Constant Ip (A) Ton (µs) WT (g) WF (m/min)
Coef. -101.19 0.09250 1.1375 -1.3692 -1.6933
SE Coef. 15.33 0.02015 0.1209 0.6044 0.6044
T -6.51 4.59 9.41 -2.27 -2.80
P 0.003 0.010 0.001 0.086 0.049
Remarks Significant Significant Significant Not significant Significant
Therefore fitted model is expressed by the following equation: ( / ) = −101 + 0.0925 + 1.14 − 1.37 − 1.69 … … … … … … (4) Since WT is not significant at 95% confidence interval, it can be removed from equation 4. Thus, the final model can be express as follows; ( / ) = −101 + 0.0925 + 1.14 − 1.69 … … … … … … (5) Table 4. ANOVA for material removal rate ‘MRR’. Source Model Residual Total
Degree of freedom 4 4 8
Sum of Squares 1074.97 35.07 1110.03
Mean Square
F-Ratio
P-Value
268.74 8.77
30.66
0.003
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1.4.3. Regression analysis of average surface roughness Table 5 presents the regression analysis for average surface roughness (Ra). Regression equation of fitted model for average surface roughness is given in equation 6. It can be seen from Table 5 that pulse-on time is most significant parameters at the confidence interval of 95%. Therefore, final model can be express as given in equation 7. Table 6 presents the ANOVA for Ra to determine the significance of regression model. P value of regression model is 0.015 which is less than 0.05. Hence regression model for Ra is significant. Table 5. Multiple regression analysis for average surface roughness (Ra). Predictor Constant Ip (A) Ton (µs) WT (g) WF (m/min)
Coef. -8.046 0.001899 0.09544 -0.03067 -0.03966
SE Coef. 1.728 0.002240 0.01344 0.06721 0.06721
T -4.66 0.89 7.10 -0.46 -0.59
P 0.010 0.467 0.002 0.672 0.587
Remarks Significant Not significant Significant Not significant Not significant
Therefore fitted model is expressed by the following equation: = −8.05 + 0.0954 − 0.0307 − 0.0397 … … … … . . (6) Since IP, WT and WF are not significant at 95% confidence interval, it can be removed from equation 6. Thus, the final model can be express as follows; = −8.05 + 0.0954 … … … … . . (7) Table 6. ANOVA for average surface roughness (Ra) Source Model Residual Total
Degree of freedom 4 4 8
Sum of squares Mean square 5.5950 1.3987 0.4337 0.1084 6.0286
F-Ratio 12.90
P-Value 0.015
1.5. Microstructure of the best finish WEDMed AISI D2 tool steel specimen Figure 5 depict the microstructure of the best finish WEDMed specimen (AISI D2 tool steel). Microstructure of the best finish WEDMed surface of AISI D2 tool steel revealed that WEDMed surface are free from burr, microcracks, nicks, asperities and voids.
Fig. 5. SEM micrograph of the best finish AISI D2 specimen machined by WEDM process.
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Conclusions This study described the experimental investigations aimed to know the variations in performance measures (i.e. MRR and Ra) with WEDM process parameters, parametric optimization of variable WEDM process parameters to maximize the material removal rate, analysis of variance (ANOVA) of MRR and Ra has been used to determine the most influencing WEDM process parameters and significant of the model on the basis of % values of P test and regression analysis to predict the MRR and Ra during machining of AISI D2 tool steel. Peak current, pulse-on-time, wire tension and wire feed rate were considered as variable process parameters having three levels each. Experiments were conducted using Taguchi L9 (34) orthogonal array. Peak current, pulse-on-time, wire tension and wire feed rate were selected as variable WEDM process parameters having three levels each to know the behaviour of performance measures namely MRR and Ra. Following conclusion can be drawn from this study: Irregular shape deeper craters, violent sparks, wire vibration and improper flushing are main factors which affect material removal rate and surface roughness. Peak current, pulse-on-time and wire feed rate have more influence on material removal rate of AISI D2 tool steel than other WEDM process parameters. Pulse-on-time has more influence on surface roughness of AISI D2 tool steel than other WEDM process parameters. Higher MRR can be achieved using higher peak current, higher pulse-on-time, lower wire feed and lower wire tension. Minimum surface roughness can be achieved using lower peak current, lower pulse-on time, higher wire feed and higher wire tension. Higher material removal rate can be achieved using 240 A peak current, 2.6 µs pulse-on-time, 900g wire tension and 5 m/min wire feed rate. Minimum surface roughness can be achieved using 60 A peak current, 0.6 µs pulse-on-time, 9 m/min wire feed rate and 1380g wire tension as optimal parameters. Acknowledgements Authors express their sincere gratitude to technical staff of Central Workshop SLIET Longowal (Punjab) for their support during preparation of workpiece (AISI D2 tool steel) and Mr. Ram Mehar for his support and help to perform the experimental runs on Electronica Sprintcut 734 WEDM machine at Shriram Industry, Ghaziabad. References [1] http://www.steelexpress.co.uk/toolsteel/D2-Steel-properties.html [2] Y. Pachaury, P. Tandon, An overview of electric discharge machining of ceramics and ceramic based composites, J. of Manuf. Proc. 25 (2017) 369–390. [3] K.H. Ho, S.T. Newman, S. Rahimifard, R.D. Allen, State of the art in wire electrical discharge machining (WEDM), International J. of Mach. Tools and Manuf. 44 (12-13) (2004) 1247–1259. [4] B.A. Schumacher, After 60 years of EDM the discharge process remains still disputed, J. of Mat. Proc. Tech. 149 (2004) 376-381. [5] A. Hasçalýk, U. Çaydas, Experimental study of wire electrical discharge machining of AISI D5 tool steel, J. of Mat. Proc. Technol. 148 (2004) 362–367. [6] S. Sarkar, S. Mitra, B. Bhattacharyya, Parametric analysis and optimization of wire electrical discharge machining of γ-titanium aluminide alloy, J. of Mat. Proc. Technol. 159 (2005) 286–294. [7] S.S. Mahapatra, A. Patnaik, Optimization of wire electrical discharge machining (WEDM) process parameters using Taguchi method, Int. J. Adv. Manuf. Technol, 34 (2007) 911-925. [8] K. Kanlayasiri, S. Boonmung, An investigation on effects of wire-EDM machining parameters on surface roughness of newly developed DC53 die steel, J. of Mat. Proc. Technol. 187–188 (2007) 26–29. [9] A. Ikram, N.A. Mufti, M.Q. Saleem, A.R. Khan, Parametric optimization for surface roughness, kerf and MRR in wire electrical discharge machining (WEDM) using Taguchi design of experiment, J. Mech. Sci. Technol. 27(7) (2013) 2133-2141, http://dx.doi.org/10.1007/s12206-013-0526-8. [10] V. Singh, S.K. Pradhan, Optimization of WEDM parameters using Taguchi technique and Response Surface Methodology in machining of AISI D2 Steel, Proc. Eng. 97 ( 2014 ) 1597 – 1608.
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[11] N. Sharma, A. Singh, R. Sharma, Deepak, Modelling the WEDM Process Parameters for Cryogenic Treated D-2 Tool Steel by integrated RSM and GA, Proc. Eng. 97 ( 2014 ) 1609 – 1617. [12] B. K. Lodhi, S. Agarwal, Optimization of machining parameters in WEDM of AISI D3 Steel using Taguchi Technique, Proc. CIRP 14 (2014) 194 – 199. [13] V. Singh, R. Bhandari1, V.K. Yadav, An experimental investigation on machining parameters of AISI D2 steel using WEDM, Int J Adv Manuf Technol, 2016, DOI 10.1007/s00170-016-8681-6. [14] R. Ramakrishnan, L. Karunamoorthy, Multi response optimization of wire EDM operations using robust design of experiments, Int. J. of Adv. Manuf. Technol. 29 (2006) 105–112. [15] P.S. Rao, K. Ramji, B. Satyanarayana, Effect of WEDM conditions on surface roughness: A parametric optimization using Taguchi method, Int. J. of Adv. Eng. Sci. and Technol. 6 (1) (2011) 041– 048. [16] S. Balasubramanian, S. Ganapathy, Grey relational analysis to determine optimum process parameters for Wire Electro Discharge Machining (WEDM), Int. J.l of Eng. Sci. and Technol. 3 (1) (2011) 95-101. [17] K.H. Ho, S.T. Newman, S. Rahimifard, R.D. Allen, State-of-the-art in wire electrical discharge machining, Int. J of Mach. Tools and Manuf. 44 (2004) 1247–1259. [18] P.H. Yu, H.K. Lee, Y.X. Lin, S.J. Qin, B.H. Yan, F.Y. Huang, Machining characteristics of polycrystalline silicon by wire electrical discharge machining, Mat. and Manuf. Proc. 26 (2011) 1443–1450. [19] A.B. Puri, B. Bhattacharyya, Modeling and analysis of the wire-tool vibration in wire-cut EDM, J of Mat. Proc. Technol. 141 (2003) 295–301. [20] Y.S. Liao, J.T. Huang, Y.H. Chen, A study to achieve fine surface finish in Wire-EDM, J of Materials Processing Technol. 149 (2004) 165–171. [21] C. Arunachalam, M. Aulia, B. Bozkurt, P.T. Eubank, Wire vibration, bowing, and breakage in wire electrical discharge machining, J of Appl. Phy. 89 (8) (2001) 4255–4262.
Appendix A. Table A1. Summary of past research works on machining of tool steel (D-series) materials using WEDM process. Authors
Workpiece and tool materials Hasçalýk et AISI D5 tool al. (2004) [5] steel
Sarkar et al. (2005) [6]
Mahapatra et al. (2007) [7]
Kanlayasiri and Boonmung (2007) [8]
Methodology
Process parameters and Responses Open circuit voltage, pulse duration, wire feed rate, dielectric pressure Surface roughness, Micro-structure
Findings
Surface roughness and micro-cracks increase with increase in pulse duration and open circuit voltage. CuZn37 brass Cracks penetration into the HAZ wire of 0.25 mm depending on pulse energy diameter WEDMed surface is harder than core material due to formation of white layer Taguchi’s L18 γ-titanium Pulse-on-time, pulse-off-time, Machining rate, surface roughness aluminide alloy Orthogonal Array peak current, servo-gap voltage, and dimensional deviation increases Additive model wire tension, dielectric pressure with increase in pulse-on-time and Brass wire of peak current whereas cutting speed 0.25mm Cutting speed, surface decrease with increase in pulse-offdiameter roughness, time dimensional deviation Surface roughness and dimensional deviation are independent of the pulse-off time. Taguchi's L27 Discharge current, pulse-onAISI D2 tool Discharge current, pulse-on-time, and time, steel orthogonal dielectric flow rate and their array and Non-linear pulse frequency, wire feed, interactions are most significant wire tension, dielectric flow rate parameters Stratified wire regression Material removal rate, (zinc coated analysis Optimized process parameters can be surface roughness, kerf width copper wire) of used for maximizing the MRR and 0.25-mm minimizing of SR and kerf width diameter DC53 die steel Full factorial Pulse-on-time, pulse-off-time, Pulse-on-time and pulse-peak current design (2k) peak current, Wire tension are most significant that affect Cu–35 wt%Zn surface roughness of DC53 die steel wire of 0.25 Surface roughness machined by WEDM mm dia. Factorial design
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Table A1. Continue… Authors Ikram et al. (2013) [9]
Workpiece and tool materials AISI D2 tool steel Brass wire of 0.25mm diameter
Singh et al. (2014) [10]
AISI D2 tool steel Brass wire of 0.25 mm diameter
Sharma et al. Cryogenic (2014) [11] treated AISI D2 tool steel CuZn37 brass wire of 0.25 mm dia. Lodhi and AISI D3 steel Agarwal Zinc coated (2014) [12] brass wire of 0.25 mm dia. Singh et al. AISI D2 tool (2016) [13] steel Brass wire of 0.25 mm diameter
Methodology
Process parameters and Findings Responses Taguchi's L18 Pulse-on-time, pulse-off-time, MRR increases and surface finish orthogonal array open voltage, wire feed rate, decreases with increase in pulse-on wire tension, dielectric pressure, time, open voltage. MRR decreases Regression analysis servo-gap voltage, with increase in servo voltage Material removal rate, Kerf increases with increase in open surface roughness, kerf width voltage, pulse-on time and wire tension. Taguchi’s L27 (34) Pulse-on-time, pulse-off-time Pulse-on time and pulse-off time are orthogonal array servo-gap voltage, wire feed most significant parameters that Response surface rate affect the MRR methodology (RSM) Pulse-on time and servo-gap voltage Material removal rate, significantly affect the surface surface roughness roughness Central composite rotatable design (CCRD) and RSM
Pulse on time, pulse-off time servo-gap voltage, peak current Surface roughness
It was observed that slope of SR curve declined with increase in peak current Surface roughness increases with increase in pulse-on-time and servogap voltage Pulse-on-time and peak current are most significant factors than other parameters.
Taguchi’s L9 Orthogonal Array
Pulse-on time, pulse-off time peak current, wire feed rate Surface roughness
Taguchi’s L27 (35) orthogonal array Response surface methodology (RSM)
Pulse-on-time, pulse-off-time, Pulse-on-time, pulse-off-time, and peak current, servo-gap servo-gap voltage are most significant voltage, parameters that affect cutting rate and wire feed rate MRR Material removal rate, Pulse-on-time and servo-gap voltage surface roughness are significantly affect the surface roughness whereas servo voltage significantly affect the gap voltage
Table A2. Values of variable WEDM process parameters and responses for experiments. WEDM process parameters
Responses
Exp.
IP
Ton
WF
WT
MRR
Ra
Runs
(A)
(µs)
(m/min)
(g)
(mm2/min)
(μm)
1
60
0.6
5
900
5.1
1.47
2
60
0.6
7
1140
12.7
2.88
3
60
2.6
9
1380
15.6
3.10
4
120
0.6
9
900
6.75
1.55
5
120
1.6
5
1380
20.6
2.95
6
120
2.6
7
900
33.9
3.55
7
180
0.6
7
1380
7.5
1.54
8
180
1.6
9
900
21.1
2.93
9
180
2.6
5
1140
38.1
3.64
24357