


ARTICLE 

Year : 2011  Volume
: 1
 Issue : 2  Page : 5964 

Parametric Optimization of CryogenicTreated D3 for Cutting Rate in Wire Electrical Discharge Machining
Hari Singh^{1}, Rajesh Khanna^{2}
^{1} Department of Mechanical Engineering, NIT, Kurukshetra, Haryana, India ^{2} Department of Mechanical Engineering, M M Engg College Mullana, Ambala, India
Date of Web Publication  24Oct2011 
Correspondence Address: Hari Singh Department of Mechanical Engineering, NIT, Kurukshetra, Haryana India
Source of Support: None, Conflict of Interest: None  Check 
Abstract   
Cryogenic treatment ("Cryo") is a supplementary process to improve the properties of metals like high carbon high chromium alloy tool steels (D3) which are increasingly used in manufacturing highperformance cutting tools (dies and punches), blanking and punching tools, extrusion tools, parts of aerospace and automotive industries, etc. The purpose of this study is to investigate the effect of parameters like pulse width, time between two pulses, maximum feed rate, servo reference mean voltage, short pulse time, and wire mechanical tension, on cutting rate (cr) of cryogenictreated D3 in wire electrical discharge machining. An L27 orthogonal array has been used to conduct experiments and statistically evaluate the experimental data by analysis of variance (ANOVA). It is seen that cr decreases with increase in pulse width, time between two pulses, and servo reference mean voltage. cr first decreases and then increases with increase in wire mechanical tension. The confirmation experiments have also been conducted to validate the results obtained by Taguchi technique. Keywords: Cryogenic treatment, maximum feed rate, pulse width, servo reference mean voltage, short pulse time, time between two pulses, wire Electricaldischarge machining
How to cite this article: Singh H, Khanna R. Parametric Optimization of CryogenicTreated D3 for Cutting Rate in Wire Electrical Discharge Machining. J Eng Technol 2011;1:5964 
How to cite this URL: Singh H, Khanna R. Parametric Optimization of CryogenicTreated D3 for Cutting Rate in Wire Electrical Discharge Machining. J Eng Technol [serial online] 2011 [cited 2020 Jul 9];1:5964. Available from: http://www.onlinejet.net/text.asp?2011/1/2/59/86633 
1. Introduction   
Electrical discharge wire cutting, more commonly known as wire Electricaldischarge machining (WEDM), is a spark erosion process used to produce two and threedimensional shapes through electrically conductive workpieces by using a wire electrode. WEDM has been an important manufacturing process for the tool, mould, and die industries. It is now increasingly used owing to its ability to produce geometrically complex shapes, as well as its ability to machine hard materials that are extremely difficult to machine using conventional processes. In the 1930s and 1940s, it was shown that this treatment can improve the performance of the tool steel ^{[1]} . Several investigators ^{[2],[3]} have focused their attention on studying this process and trying to raise the efficiency of tool steels through cryogenics. Most researchers ^{[4],[5]} agree that cryogenic treatment can improve the performance of the tools. Improvement of the wear resistance of the tool steels was the most significant effect of this treatment. Some industries like aerospace, automotive, and electronic have used this process in their production line to improve wear resistance and dimensional stability of components ^{[6]} . Manna and Bhattacharyya ^{[7]} found open gap voltage and pulse on period as the most significant machining parameters for controlling the metal removal rate using Taguchi methodbased analysis for WEDM on Al/SiCMMC. Ramakrishnan and Karunamoorthy ^{[8]} reported the development of artificial neural network (ANN) models and multiresponse optimization technique to predict and select the best cutting parameters in WEDM process.
Scott et al. ^{[9]} used a factorial design requiring a number of experiments to determine the most favorable combination of the WEDM parameters. They found that the discharge current, pulse duration, and pulse frequency are the significant control factors affecting the MRR and SF, while the wire speed, wire tension, and dielectric flow rate have the least effect. Liao et al. ^{[10]} proposed an approach of determining the parameter settings based on the Taguchi quality design method and the analysis of variance. The results showed that the MRR and SF are easily influenced by the table feed rate and pulse ontime.
Rajurkar and Wang ^{[11]} analyzed the wire rupture phenomenon with a thermal model. An extensive experimental investigation has been carried out to determine the variation of machining performance outputs, namely MRR and surface finish, with machining parameters in the study. Spedding and Wang ^{[12]} optimized the process parameter settings by using ANN modeling to characterize the WEDM workpiece surfaces.
Barron ^{[13]} concluded that cryogenic process uses sub zero temperature down to 196°C in a supercooled bath containing liquid nitrogen and is used for treating wide range of metal components including hot die steel.
Kamody ^{[14]} reported a process for the treatment of materials to improve stability, shockability and hardness, and extended wearability. Molinari et al., ^{[15]} in their study, reported that cryogenic treatment improves the surface hardness and thermal stability of the materials. Kamody ^{[16]} describes the effect of cryogenic treatment to minimize the instability effects of workpiece, which results in regulation and compensation of wire burning action.
The review of literature indicates that there is limited published work on the effect of machining parameters on cutting rate (cr) in WEDM in a cryogenic cutting environment. In this study, the effect of the machining parameters and their level of significance on the cr are statistically evaluated by using analysis of variance (ANOVA). Experiments were conducted to render best cr while machining cryogenically treated D3 material.
2. Experimental Methodology   
Cryogenic processor (CP200LH), as shown in [Figure 1], was used for cryogenic treatment of workpiece. The different sets of experiments were performed using a Robofil 290 Charmilles Technologies WEDM machine, as shown in [Figure 2]. During the experiments, the cr of the workpiece was measured. The work material, electrode, and the other machining conditions are as follows:
 Workpiece: High carbon high chromium alloy tool steel (D3)
 Electrode (tool): 250 μm φ, CuZn37 Master Brass wire (900 N/mm ^{2} tensile strength)
 Workpiece height: 30 mm
 Dielectric conductivity: 20 mho
 Cutting voltage (V): 80 V
 Dielectric temperature: 2225°C
 Injection pressure set point was at 4 (around 6.5 bars)
 Ignition pulse current (IAL) at 8 Amp.
2.1 Deep Cryogenic Treatment
 D3 material as shown in [Figure 3] used in WEDM is placed in cryogenic processor (CP200LH).
 In ramp, down stage temperature is decreased at the rate 0.39°C per minute from room temperature (25°C Aprox.).
 Temperature is decreased up to 184°C in 9 hours in cryogenic processor.
 A typical soak segment will hold the temperature 184°C for a period of 18 hours.
 Temperature is increased at the rate of 0.39°C per minute in ramp up stage for a period of 9 hours.
 Temperature is bought to room temperature (25°C Aprox.).
An orthogonal array L27 (3^{13} ) has been employed according to the Taguchi method based robust design philosophy to evaluate the main influencing factors that affect the cr. A set of six WEDM parameters with three levels for control factors, such as factor A (pulse width), factor B (time between two pulses), factor A _{j} (servo reference mean voltage), factor T _{ac} (short pulse time), factor S (maximum feed rate), and factor W _{b} (wire mechanical tension), are considered as the controlling factors for optimal analysis during machining of D3. [Table 1] shows the various control factors and their levels selected while experimenting.
3. Results and Discussions   
The WEDM experiments were conducted by using the parametric approach of the Taguchi's Method. The effects of individual WEDM process parameters, on the selected quality characteristic, cr, have been discussed in this section. The average value and S/N ratio of the response characteristic for each variable at different levels were calculated from experimental data. The analysis of variance (ANOVA) of raw data and S/N data is carried out to identify the significant variables and to quantify their effects on the response characteristic. The most favorable values (optimal settings) of process variables in terms of mean response characteristic were established by analyzing the response curves and the ANOVA Tables.
The experimental data are given in [Table 2]. The average values of cr for each parameter at levels 1, 2, and 3 for raw data and S/N ratios are plotted in [Figure 4] and [Figure 5]. It is seen that cr decreases with increase in pulse width, time between two pulses, and servo reference mean voltage. cr first decreases and then increases with increase in wire mechanical tension.
3.1 Selection of Optimal Levels
In order to study the significance of the process variables toward cr, analysis of variance (ANOVA) was performed as shown in [Table 3] and [Table 4]. It was found that maximum feed rate and wire mechanical tension are nonsignificant process parameters for cr. Nonsignificant parameters were pooled and the pooled versions of ANOVA of the S/N data and the raw data for cr are given in [Table 5] and [Table 6], respectively. From these tables, it is observed that pulse width, time between two pulses, servo reference mean voltage, and wire mechanical tension significantly affect both the mean and the variation in the cr values. Time between two pulses has the greatest effect on cr and is followed by servo reference mean voltage, pulse width, and wire mechanical tension in that order. As cr is the "larger the better" type quality characteristic, from the experiments, it can be seen from the [Table 7] that the first level of pulse width (A _{1} ), first level of time between two pulses(B _{1} ), first level of servo reference mean voltage (A _{j})_{1} , and first level of wire mechanical tension (W _{b} ) _{1} provide maximum value of cr. The S/N ratio analysis also suggests the same levels of the variables [A _{1} , B _{1} , (A _{j} ) _{1} and (W _{b} ) _{1} ] as the best levels for maximum cr in WEDM process.
3.2 Optimum Value of Cutting Rate
The optimum value of cr is predicted at the selected levels of significant variables: Pulse width (A _{1}), time between two pulses (B _{1} ), servo reference mean voltage (A _{j} ), and short pulse time (T _{ac}) [Table 6]. The estimated mean of the response characteristic (cr) can be determined as:
Where, = overall mean of cutting rate = (ΣR _{1} + ΣR _{2} + ΣR _{3} )/81 = 1.4975mm/min
Where, R _{1} , R _{2} , and R _{3} values are taken from the [Table 2], and the values of , and (T _{ac})_{3} are taken from the Taguchi's experimental data.
=average value of cutting rate at the first level of pulse width=1.578 mm/min
= average value of cutting rate at the first level of time between two pulses=1.789 mm/min
=average value of cutting rate at the first level of servo reference mean voltage=1.644 mm/min
(W _{b} ) _{1} =Average value of cutting rate at the third level of wire mechanical tension=1.604 mm/min
Substituting the values of various terms in the above equation,
μ _{cr} =1.578+1.789 + 1.644 + 1.6043 (1.4975) = 2.1225 mm/min
The 95% confidence intervals of confirmation experiments (CI _{CE}) and population (CI _{POP}) are calculated as below:
Where, F_{α}(1, f _{e}) = The F ratio at the confidence level of (1α) against DOF 1 and error degree of freedom f _{e}.
N = Total number of results=27 × 3=81, R=Sample size for confirmation experiments = 3
V _{e} =Error variance=0.01523; f _{e} =error DOF=18 [Table 6]
F _{0.05} (1, 18) = 4.4139 (Tabulated F value (Ross, 1996))
So, CI _{CE} = ± 0.1728, and CI _{POP}=± 0.08642
Therefore, the predicted confidence interval for confirmation experiments is:
Mean μ _{cr} CI _{CE} < μ_{cr} < Mean μ_{cr} +CI _{CE} i.e. 1.9497 < μ_{cr} <2.2953
The 95% confidence interval of the population is:
Mean μ _{cr} CI _{POP}< μ _{cr} < Mean μ _{cr} +CI _{POP} i.e. 2.03608 < μ _{cr} < 2.20892
The optimal values of process variables at their selected levels are as follows:
(A _{1} ): 0.8 machine units; (B _{1} ): 6.6 machine units; (A _{j} ) _{1} : 34; (W _{b} ) _{1} :0.8.
4. Confirmation Experiment   
Conducting a verification experiment is a crucial, final, and indispensable part of the Taguchi methodoriented robust design project. Its aim is to verify the optimum condition suggested by the matrix experiment estimating how close are the respective predictions with the real ones. However, if the observed S/N ratios under the optimum conditions differ drastically from their respective predictions, the additive model proves to be a failure eventually. The S/N values were predicted under the optimum condition for the above case study. Also, S/N values were estimated from the machining results under optimum parametric settings. The results are tabulated in [Table 8]. It is clear that the data agree very well with the predictions. Therefore, the optimum settings given in [Table 8] may be adopted and implemented accordingly.  Table 8: Predicted optimal values, confidence intervals, and results of confirmation experiments
Click here to view 
5. Conclusion   
Influences of WEDM machining variables on cr of newly developed cryogenictreated high carbon high chromium alloy tool steel (D3) were investigated in this paper. The machining variables included pulse width, time between two pulses, servo reference mean voltage, short pulse time, maximum feed rate, and wire mechanical tension. The variables affecting the cr significantly were identified using ANOVA technique. Results showed that time between two pulses, servo reference mean voltage, pulse width, and wire mechanical tension were significant variables to the cr of wireEDMed D3, high carbon high chromium alloy tool steel. The cr decreases with increase in pulse width, time between two pulses, and servo reference mean voltage. cr first decreases and then increases with increase in wire mechanical tension. A _{1} 0.8 units, B _{1} 6.6 units, A _{j} 34 V, and W _{b} 0.8 is the optimized setting of machine tool for the present study.
References   
1.  C. Wilkins, "Cryogenic processing: The big chill", EDM Today, pp. 3644, 1999. 
2.  D. N. Collins, "Cryogenic treatment of tool steels", Adv. Mater. Process, pp. H23H29, 1998. 
3.  K. Moore, and D. N. Collins, "Cryogenic treatment of three heat treated tool steels", Key Eng. Mater., pp. 4754, 8687, 1993. 
4.  D. J. Kamody, "Cryogenics process update", Adv. Mater Process, pp. H67 H69, 1999. 
5.  T. P. Sweeney Jr, "Deep cryogenics: The great cold debate", Heat treating, pp. 2832, 1986. 
6.  D. Mohan Lal, S. Renganarayanan, and A. Kalanidhi, "Cryogenic treatment to augment wear resistance of tool and die steels", Cryogenics, Vol. 41, pp. 149155, 2001. 
7.  A. Manna, and B. Bhattacharyya, "Taguchi and Gauss elimination method: A dual response approach for parametric optimization of CNC wire cut EDM of PRAlSiCMMC", International Journal of Advanced Manufacturing Technology, Vol. 28, pp. 6775, 2006. 
8.  Ramakrishnana and Krunamoorthy, "Modeling and multiresponse optimization of Inconel 718 on machining of CNC WEDM process", Journal of Materials Processing Technology, Vol. 207, pp. 343349, 2008. 
9.  D. Scott, S. Boyina, and K. P. Rajurkar, "Analysis and optimisation of parameter combination in wire electrical discharge machining", International Journal of Production Research, Vol. 29, no. 11, pp. 21892207, 1991. 
10.  Y. S. Liao, J. T. Huang, and H. C. Su, "A study on the machiningparameters optimization of wire electrical discharge machining", Journal of Material Processing Technology, Vol. 71, pp. 487493, 1997. 
11.  K. P. Rajurkar, and W. M. Wang, "Thermal modeling and online monitoring of wireEDM", Journal of Material Processing Technology, Vol. 38, pp. 417430, 1993. 
12.  T. A. Spedding, and Z. Q. Wang, "Parametric optimization and surface characterization of wire electrical discharge machining process", Precision Engineering, Vol. 20, no. 1, pp. 515, 2001. 
13.  R. Barron, "Cryogenic treatment of metals to improve wearresistance", Cryogenics, Vol. 22, pp. 409413, 1982. 
14.  D. Kamody, "Process for the cryogenic treatment of metal containing materials", US Patent #5 259200, 1993. 
15.  A. Molinari, M. Pellizzari, S. Gialenella, G. Straffelini, and K. H. Stiasny, "Effect of deep cryogenic treatment on the mechanical properties of tool steels", Journal of Materials Processing Technology, Vol. 118, pp. 350355, 2001. 
16.  D. Kamody, "Cryogenic treatment enhances stability for wire EDM operation", MMS online, 2001. 
Authors   
Dr. Hari Singh is an Associate Professor in Mechanical Engineering Department at National Institute of Technology, Kurukshetra. He graduated in 1987; post graduated in 1994 and got PhD in 2001 in area of Production and Industrial Engineering. His areas of interest include conventional and unconventional metal cutting, process and product optimization, experimental designs, optimization techniques. He has guided 5 PhDs and 20 MTechs in his area of research. He has published more than 80 research papers in various national and international journals of repute and conference proceedings. He has 24 years teaching and research experience.
Rajesh Khanna is a research scholar in Mechanical Engineering Department at National Institute of Technology, Kurukshetra. He did his B.Tech. in 1997, M.Tech. in 2005 and currently pursuing PhD (Part time) in the area of Wire cutEDM since 2006. He is teaching in Mechanical Engineering Department of M.M. University, Mullana Ambala. He has 13 years of teaching experience. His area of interest is nonconventional machining.
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7], [Table 8]
This article has been cited by  1 
Multiobjective Optimization of Process Parameters in Wire Electric Discharge Machining of Ti6242 Alloy 

 M. P. Garg, Ajai Jain, Gian Bhushan   Arabian Journal for Science and Engineering. 2014; 39(2): 14651476   [Pubmed]  [PDF]  [DOI]   2 
Modeling and Parametric Investigation of WEDM for D2 Tool Steel Using RSM and GA 

 Neeraj Sharma, Neeraj Ahuja, Sorabh Gupta, Ajit Singh, Renu Sharma   Applied Mechanics and Materials Vols. 2014; 292294: 511515   [Pubmed]  [DOI]   3 
WEDM Development and Optimization of process: A review 

 Lokesh Goyat   International Journal of Innovations in Engineering and Technology (IJIET). 2013; 2(3): 7985   [VIEW]  [PDF]  [DOI]   4 
Investigation and Optimization of Materiel Removal Rate For Wire Cut Electro Discharge Machining In EN5 Steel Using Response Surface Methodology 

 Ravinder Chaudhary   International Journal of Latest Trends in Engineering and Technology (IJLTET). 2013; 1(1): 192199   [VIEW]  [DOI]   5 
Multiresponse optimization of process parameters based on response surface methodology for pure titanium using WEDM process 

 Anish Kumar, Vinod Kumar, Jatinder Kumar   International Journal of Advanced Manufacturing Technology. 2013; March   [Pubmed]  [DOI]   6 
Modeling and multiresponse optimization on WEDM for HSLA by RSM 

 Neeraj Sharma, Rajesh Khanna, Rahul Dev Gupta, Renu Sharma   Int J Adv Manuf Technol. 2012;   [Pubmed]  [DOI]   7 
A Parametric Optimization of Electric Discharge Drill Machine Using Taguchi Approach 

 Samar Singh, Mukesh Verma   Journal of Engineering, Computers & Applied Sciences (JEC&AS). 2012; 1(3): 3947   [VIEW]  [PDF]  [DOI]   8 
Effect of Cutting Parameters on MRR and Surface Roughness in Turning EN8 

 Hardeep Singh, Rajesh Khanna, M.P. Garg   Current Trends in Engineering Research. 2011; 1(1): 100104   [VIEW]  [PDF]   9 


   . ;   [Pubmed]  



