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Prediction and optimization of surface roughness and MRR in wire EDM of maraging steel 350
Materials Today: Proceedings xxx (xxxx) xxx
Contents lists available at ScienceDirect
Materials Today: Proceedings journal homepage: www.elsevier.com/locate/matpr
Prediction and optimization of surface roughness and MRR in wire EDM of maraging steel 350 K. Dayakar ⇑, K.V.M. Krishnam Raju, Ch. Rama Bhadri Raju Department of Mechanical Engineering, SRKR Engineering College, Bhimavaram 534204, India
a r t i c l e
i n f o
Article history: Received 9 May 2019 Received in revised form 15 June 2019 Accepted 17 June 2019 Available online xxxx Keywords: WEDM ANOVA Surface roughness (Ra) MRR Taguchi
a b s t r a c t Wire EDM (WEDM) is a special method of electrical discharge machining. Maraging steel had very-low carbon steel which consists of more material toughness and strength. In present work a Taguchi optimization method proposed for WEDM of maraging steel 350 and the machining output responses are Material Removal Rate (MRR) and Surface roughness (Ra). Machining process is planned by Taguchi’s robust design. The analysis is performed for the results obtained by using analysis of variance (ANOVA). The optimum conditions for this work are performed through Taguchi analysis. Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 2nd International Conference on Applied Sciences and Technology (ICAST-2019): Materials Science.
1. Introduction
2. Literature review
Wire EDM (WEDM), commonly called Electric Discharge Wire Cutting (WDWC) and is a special method of electrical discharge machining used a small wire diameter as the electrode for cutting a small kerf in the work. The cutting method in wire EDM is processed by thermal energy from electric discharge among the electrode wire and the workpiece. The workpiece is fed past the wire to achieve the desired cutting path; somewhat in the manner of a band saw operation. Numerical control is used to control the work part motions during cutting. As it cutting wire gradually and continuously advanced within a supply spool and take-up spool to present a recent electrode with no changes in diameter to the work. This helps to control a constant slot width during cutting. As in Electric discharge machine, wire EDM needs to be carried out in the existence of dielectric flow. This is applying through nozzles controlled at the work material is immersed in a dielectric bath. Wire diameter bounds from 0.0716 to 0.30 mm; depending on prescribed kerf width electrode materials are tungsten, copper, brass, and molybdenum. Work part toughness and Hardness do not issue the cutting performance. The only need is material should be electrically conductive. The schematic diagram of WEDM represented in Fig. 1 (see Table 1).
From the literature reviews for MRR and Ra, some parameters influence very much those parameters such as pulse on time, pulse off time, peak current, spark gap voltage is selected in the present work to analyse the most influential parameters for MRR and Ra. Taguchi design of experiments is the most commonly used method in the experimental procedure and to obtain the mathematical equations and significance of parameters are identified through ANOVA.
⇑ Corresponding author. E-mail address: [email protected] (K. Dayakar).
3. Material selection Maraging steel is a very low carbon content steel that has high material strength and toughness The term ’maraging’ represents for strengthening mechanisms; ’mar’ indicates to martensite and ’aging’ for a heat treatment called age hardening. Maraging steels are hugely sought in applications that need precise and special geometries and demand operation at high loads. Applications are Rocket motor casings, Take-off and landing gear in aircraft, fencing blades, etc (see Table 2). 4. Experimental setup and methodology Experiments are machined on Ultra-cut S1 Wire cut EDM CNC machine, making by Electronica (India). The electrode
https://doi.org/10.1016/j.matpr.2019.06.635 2214-7853/Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 2nd International Conference on Applied Sciences and Technology (ICAST-2019): Materials Science.
Please cite this article as: K. Dayakar, K. V. M. Krishnam Raju and C. Rama Bhadri Raju, Prediction and optimization of surface roughness and MRR in wire EDM of maraging steel 350, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.635
2
K. Dayakar et al. / Materials Today: Proceedings xxx (xxxx) xxx
Fig. 1. Schematic diagram of WEDM.
Table 1 A literature review. S no
Author
Type of material used
Design of experiments used technique
Input process parameters
Output response
Find out from the study
1.
Alagarswamy et al. [1]
AA7075/ B4CP MMC
Pulse on time, pulse off time, spark voltage, wire tension
Surface roughness
Pulse on time is the most significant parameter
2.
Gijoy et al. [2]
SS304
Taguchi L9 orthogonal array Taguchi L9 orthogonal array
Pulse on time, pulse off time, wire feed.
Wire feed is most influenced parameter on MRR; pulse on time is the most influenced parameter on surface roughness.
3.
Mathew et al. [3]
AISI 304
Taguchi L 27
Pulse on time, pulse off time, spark gap voltage, water pressure, wire feed, wire tension
4.
Johnson et al. [4]
AL6061
Taguchi L18
5.
Arun Kumar et al. [5]
Berlium copper alloy
RSM29 experiments
Pulse on time, the pulse of time, feed rate, wire tension. Pulse on time. The pulse off time, spark gap voltage.
6.
AISI 316 SS
Taguchi L27
7
Padmavathi et al. [6] Kumar et al. [7]
Inconel x750
Taguchi L27
8.
Kumar et al. [8]
EN-31
Taguchi L16
Pulse on time, pulse off time, spark gap voltage, wire feed
Surface roughness, the material removal rate Surface roughness and material removal rate Surface roughness Surface roughness and material removal rate. Material removal rate Surface roughness and cutting speed Material removal rate
9.
Kapoor et al. [9]
Taguchi L9
10.
Rao et al. [10]
M-35 HSS 5% cobalt Aluminum alloy
Pulse on time, pulse off time, spark gap voltage, peak current. Pulse on time, pulse off time, flushing pressure, wire feed, wire tension, spark gap voltage, servo feed rate.
Taguchi L18
Pulse on time, pulse off time, peak current, wire tension, wire feed rate. Pulse on time, pulse off time, servo voltage, peak current, wire feed, wire T
Material removal rate Surface roughness and material removal rate
Pulse on time contributes to the material removal rate and surface roughness.
Surface roughness influenced by pulse on time. Pulse on time significances on surface roughness and MRR
Peak current and pulse on time contribute on MRR Pulse on time, the pulse of time, servo voltage and peak current are more contributes to cutting speed and MRR Maximum MRR at pulse on time 120, pulse off time 25, wire feed 11, spark gap voltage 40. Peak current is the most significant parameter Pulse on time, peak current and spark gap voltage are the most significant parameters on surface roughness and MRR
Table 2 Composition of maraging steel 350. Chemical
C
Si
Mn
Ni
Co
Mo
Ti
Al
Fe
%
<0.03%
<0.10%
<0.10%
<18.50%
<12.00%
<4.80%
<1.40%
<0.10%
Reminder
material used as brass with diameter 0.25 mm; demineralized water is a dielectric fluid. Design of experiments is based on Taguchi L27 orthogonal array model. Output response character-
ization is by ANOVA to find the input process parameters that effect on Ra and MRR. After complete of machining, Ra is measured by surface roughness Tester model SJ210. The MRR is
Please cite this article as: K. Dayakar, K. V. M. Krishnam Raju and C. Rama Bhadri Raju, Prediction and optimization of surface roughness and MRR in wire EDM of maraging steel 350, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.635
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K. Dayakar et al. / Materials Today: Proceedings xxx (xxxx) xxx
Fig. 2. Specimens after machining.
obtained by Eq. (1) and Fig. 2 Shows Experimental specimens after machining. MRR ¼ ð2 Wg þ DÞ t VC mm3 =min
ð1Þ
where ’Wg’ is spark gap (mm), ’D’ is the wire electrode diameter in mm,’t’ is the thickness of workpiece in mm, ’VC’ is cutting speed in mm/min. The cutting speed is displayed on the computer while machining. The size of the workpiece is 10 9 9 in mm. The optimization is performed by the Taguchi method for obtaining the optimal conditions for lower surface roughness and higher material removal rate. Four input process parameters are being selected for experimentation which is given in the Table 3. By using experimental data mathematical models and its corresponding equations and S/N plots are obtained by Minitab software. The following table contents the taking parameters and their levels 5. Design of experiments for surface roughness and material removal rate 5.1. Analysis of variance ANOVA (Analysis of Variance) is an analytical way which describes the factors significantly affecting the experimental results. ANOVA consists of All Experimental Data distribution for summing squares. Unbiased variance Decomposing of the total sum to sums of squares that are considering all elements used in the analysis. Calculates unbiased variances over sums of squares considering all elements over their DOF. calculating the variance ratio by dividing each unbiased variance by the error variance;
Searching which factors significantly affect experimental results by analysing the error variance. This procedure can be polished by design an ANOVA table. 5.2. Taguchi analysis Design of parameter is the process of investigation leading to the establishment of optimal values of the design parameters so that the product/process perform on target and is not influenced by the noise factors. Statistically designed experiments and/or orthogonal experiments are used for this purpose. 5.2.1. 5.2.1Smaller-the better Here, the quality characteristic is non-negative and continues. It can take any value among 0 to 1. The desired value (the target) is zero. These problems are characterized by the scaling factor (ex: surface roughness, pollution, tire wear, etc.). The S/N ratio (ɳ) is given below. Ni X y2k SNi ¼ 10log Ni k¼1
! ð2Þ
5.2.2. Larger-the better The condition characteristic is non-negative and continuous. It can take any Y2i Value from 0 to 1. The ideal target value of this form of quality characteristic is _ (as large as possible). Quality characteristics like strength values, fuel efficiency, etc. are examples of this type. In these problems, there is no scaling factor. The S/N ratio (ɳ) is given below
SNi ¼ 10log
1X 1 n Y 2i
! ð3Þ
Table 3 Process parameters and their levels. S no
Parameters
Units
Symbol
Level 1
Level 2
Level 3
1 2 3 4
Pulse on time (TON) Pulse off time (TOFF) Peak current (IP) Spark gap voltage (SV)
Micro seconds Microseconds Ampere Volts
l sec l sec
100 55 10 20
108 59 11 50
108 63 12 80
A V
Please cite this article as: K. Dayakar, K. V. M. Krishnam Raju and C. Rama Bhadri Raju, Prediction and optimization of surface roughness and MRR in wire EDM of maraging steel 350, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.635
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Table 4 Input parameter values for Ra and MRR experimental and predicted results. S no
T on
T off
IP
SV
Ra
Predicted Ra
MRR
Predicted MRR
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
100 100 100 100 100 100 100 100 100 104 104 104 104 104 104 104 104 104 108 108 108 108 108 108 108 108 108
55 55 55 59 59 59 63 63 63 55 55 55 59 59 59 63 63 63 55 55 55 59 59 59 63 63 63
10 11 12 10 11 12 10 11 12 10 11 12 10 11 12 10 11 12 10 11 12 10 11 12 10 11 12
20 50 80 50 80 20 80 20 50 20 50 80 50 80 20 80 20 50 20 50 80 50 80 20 80 20 50
3.895 1.982 1.276 2.669 1.809 1.463 3.47 2.01 1.424 3.349 2.757 2.974 3.046 2.484 2.798 3.724 2.574 1.748 3.361 1.684 1.678 2.862 2.177 1.475 3.716 3.269 1.853
3.301 1.799 1.713 2.596 1.923 1.57 3.295 2.355 1.441 3.907 2.406 2.32 3.203 2.529 2.177 3.901 2.961 2.047 3.532 2.03 1.944 2.827 2.153 1.801 3.526 2.586 1.672
0.516 2.012 2.227 0.580 1.702 1.660 0.648 1.365 1.509 0.590 2.012 2.835 0.541 2.025 2.029 0.445 1.660 2.051 0.516 2.128 2.349 0.657 1.822 2.029 0.405 1.476 2.012
0.483 1.923 2.307 0.429 1.791 1.778 0.371 1.337 1.799 0.701 2.142 2.525 0.648 2.01 1.997 0.59 1.555 2.018 0.613 2.054 2.437 0.56 1.922 1.909 0.502 1.467 1.929
Ra ¼ 2:5010 0:279 T on 100 þ 0:327 T on 104
6. Results
0:048 T on 108 þ 0:050 T off 55
6.1. Experimental results
0:192 T off 59 þ 0:142 T off 63 þ 0:843 IP 10
The following Table 4 consist of given input process parameters and their levels and experimental results and predicted results.
0:196 IP 11 0:647 IP 12 þ 0:187 sv 20 0:276 sv 50 þ 0:089 sv 80
ð4Þ
6.2. Analysis of variance 6.2.1. For surface roughness The following Table 5 constructed from experimental data by using analytical software for surface roughness. From the ANOVA Table 5 for surface roughness (Ra), mainly influenced on peak current (IP), which contributes 61.91%, and for a pulse on time (Ton) which it contributes 9.94%. Pulse off time (T off) contributes 3.15%; spark gap voltage (SV) contributes 6.32% with an error of 18.68%. Regression equation:
Table 7 Response table for signal to noise ratios. Level
T on
T off
IP
SV
1 2 3 Delta Rank
6.310 8.864 7.318 2.553 2
7.577 6.974 7.941 0.968 4
10.422 7.072 4.997 5.425 1
8.113 6.655 7.724 1.459 3
Table 5 ANOVA table for Ra. Source
DF
Seq SS
Contribution
Adj SS
Adj MS
F-Value
P-Value
T on T off IP SV Error Total
2 2 2 2 18 26
1.6852 0.5350 10.4981 1.0720 3.1679 16.9580
9.94% 3.15% 61.91% 6.32% 18.68% 100.00%
1.6852 0.5350 10.4981 1.0720 3.1679
0.8426 0.2675 5.2490 0.5360 0.1760
4.79 1.52 29.83 3.05
0.022 0.246 0.000 0.073
Table 6 ANOVA table for MRR. Source
DF
Seq SS
Contribution
Adj SS
Adj MS
F-Value
P-Value
T on T off IP SV Error Total
2 2 2 2 18 26
0.2181 0.7343 12.0174 0.3891 0.4649 13.8238
1.58% 5.31% 86.93% 2.81% 3.36% 100.00%
0.2181 0.7343 12.0174 0.3891 0.4649
0.10905 0.36714 6.00869 0.19454 0.02583
4.22 14.21 232.64 7.53
0.031 0.000 0.000 0.004
Please cite this article as: K. Dayakar, K. V. M. Krishnam Raju and C. Rama Bhadri Raju, Prediction and optimization of surface roughness and MRR in wire EDM of maraging steel 350, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.635
K. Dayakar et al. / Materials Today: Proceedings xxx (xxxx) xxx Table 8 Response table for signal to noise ratios. Level
T on
T off
IP
SV
1 2 3 Delta Rank
1.5673 2.3470 1.9524 0.7797 4
2.8982 2.1086 0.8598 2.0385 2
5.3753 5.0180 6.2239 11.5991 1
1.2378 2.3240 2.3049 1.0861 3
5
6.2.2. For material removal rate The following Table 6 constructed from experimental data by using analytical software for Material removal rate. From the ANOVA table for MRR, highly influenced on peak current (IP) which contributes 86.93%, and for pulse off time (T off) contributes 5.31%. Spark gap voltage (SV) contributes 3.36% and pulse on time (Ton) contributes 1.58% with error 3.36%. Regression equation (see Table 7):
Fig. 3. Variation of Surface Roughness with other parameters.
Fig. 4. Variation of SN ratios with Surface Roughness.
Please cite this article as: K. Dayakar, K. V. M. Krishnam Raju and C. Rama Bhadri Raju, Prediction and optimization of surface roughness and MRR in wire EDM of maraging steel 350, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.635
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Fig. 5. Variation of MRR with other parameters.
Fig. 6. Variations for SN ratios with MRR.
MRR ¼ 1:4745 0:1165 T on 100 þ 0:1023 T on 104
6.3. Taguchi analysis
þ 0:0141 T on 108 þ 0:2131T off 55 0:0246 T off 59 0:1886 T off 63 0:9298 IP 10 þ 0:3260 IP 11 þ 0:6037 IP 12 0:1584 sv 20 þ 0:0262 sv 50 þ 0:1321 sv 80
ð5Þ
6.3.1. For surface roughness From the response Table 8, SN ratios for Ra Taguchi analysis calculated by smaller is better Eq. (2) the ranking is given by Minitab software 18. The ranks are peak current (IP) 1, pulse on time (Ton) 2, spark gap voltage (SV) 3, pulse off time (T off) 4.
Please cite this article as: K. Dayakar, K. V. M. Krishnam Raju and C. Rama Bhadri Raju, Prediction and optimization of surface roughness and MRR in wire EDM of maraging steel 350, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.635
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Fig. 7. Interaction plot for MRR.
Fig. 8. Interaction plot for surface roughness.
Please cite this article as: K. Dayakar, K. V. M. Krishnam Raju and C. Rama Bhadri Raju, Prediction and optimization of surface roughness and MRR in wire EDM of maraging steel 350, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.635
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Fig. 9. For Experimental Ra vs. Predicted Ra.
Fig. 10. For Experimental MRR vs. Predicted MRR.
6.3.2. For material removal rate From SN ratio response table for surface roughness Taguchi analysis calculated by larger is better Eq. (3) the ranking is given by Minitab software 18. The ranks are peak current (IP) 1, pulse off time (T off) 2, spark gap voltage (SV) 3, pulse on time (Ton) 4. Fig. 3 shows the variation of surface roughness in each input process parameters and Fig. 4 shows the optimum values for surface roughness in each input process parameter. Fig. 5 shows the variation of MRR in each input process parameters and Fig. 6 shows the optimum values for MRR in each input process parameter. Fig. 7 shows the intractions in each input process parameter for surace roughness, Fig. 8 shows intractions in each input parameters for MRR. Fig. 9 indicates the comparison graph from experimental data and predicted data for surface roughness and Fig. 10 shows comparison graph from experimental data and predicted data for MRR.
7. Conclusions This work is performed by using design of experiments on maraging steel 350 which is having high strength and toughness compared to other materials. This paper mainly focused in Ra and MRR by varying input process parameters pulse on time, pulse off time, peak current and spark gap voltage. The following are the conclusions drawn from this work.
Surface roughness increases when pulse on time and peak current increases. The Optimum conditions for surface roughness (Ra) are obtained at pulse on time 100 lsec, pulse off time 59 lsec, peak current 12 A and spark gap voltage 50 V. Peak current is the highest influenced parameter on surface roughness. MRR increases when Pulse on time, spark gap voltage and peak current increases. The Optimum conditions for MRR are obtained at pulse on time 104 lsec, pulse off time 55 lsec, peak current12 A and spark gap voltage 50 V. Peak current is the highest influenced parameter on MRR. References [1] S.V. Alagarsamy, M. Ravichandran, S. Vignesh, S.A.V. Sagayaraj, Multiperformance optimization of wire cut EDM process parameters on surface roughness of AA7075/B4Cp metal matrix composites, Magnesium, 2, 2–9. [2] S. Gijoy, S.S. Abhilash, S. Hari Krishnan, Optimization of wire electrical discharge machining process parameters using Taguchi method, Int. J. Curr. Eng. Sci. Res. (IJCESR) 4 (7) (2017). [3] Bijo Mathew, B.A. Benkim, J. Babu, Multiple process parameter optimization of WEDM on AISI304 using utility approach, Procedia Mater. Sci. 5 (2014) 1863– 1872. [4] Johnson Jerin, K.T. Bibin, Anoop Sankar, Optimization of wire electric discharge machining parameters on Al 6061, Int. J. Eng. Sci. Res. Technol. (2018). [5] N.E. Arun Kumar, Suresh Babu, Murali, A study on parametric optimization of wire electrical discharge machining using response surface methodology, t 1051-1059issn 0972-768x J. Chem. Sci. 14 (2) (2016).
Please cite this article as: K. Dayakar, K. V. M. Krishnam Raju and C. Rama Bhadri Raju, Prediction and optimization of surface roughness and MRR in wire EDM of maraging steel 350, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.635
K. Dayakar et al. / Materials Today: Proceedings xxx (xxxx) xxx [6] K.R. Padmavathi, S. Devaraj, I. John Solomon, A. Premkumar, Influence of process parameters on wire EDM process for AISI 316 stainless steel, Int. J. Eng. Res. Technol. (IJERT) 6 (02) (2018), Special Issue. [7] M. Kumar, H. Singh, Multi response optimization in wire electrical discharge machining of Inconel X-750 using Taguchi’s technique and grey relational analysis, Cogent Eng. 3 (1) (2016) 1266123. [8] Surinder Kumar, Parveen Kumar, Investigation of material removal rate for wire-cut EDM of EN-31 alloy steel using Taguchi technique, Int. J. Sci. Eng. Res. 7 (12) (2016).
9
[9] Shrey Kapoor, Sharad Shrivastava, Optimisation and influence of process parameters for machining with wire cut EDM using Taguchi’s technique, Int. J. Adv. Res. Sci. Eng. Technol. 5 (6) (June 2018). [10] P.S. Rao, K. Ramji, B. Satyanarayana, Experimental investigation and optimization of wire EDM parameters for surface roughness, MRR and white layer in machining of aluminium alloy, Procedia Mater. Sci. 5 (2014) 2197–2206.
Please cite this article as: K. Dayakar, K. V. M. Krishnam Raju and C. Rama Bhadri Raju, Prediction and optimization of surface roughness and MRR in wire EDM of maraging steel 350, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.635
Contents lists available at ScienceDirect
Materials Today: Proceedings journal homepage: www.elsevier.com/locate/matpr
Prediction and optimization of surface roughness and MRR in wire EDM of maraging steel 350 K. Dayakar ⇑, K.V.M. Krishnam Raju, Ch. Rama Bhadri Raju Department of Mechanical Engineering, SRKR Engineering College, Bhimavaram 534204, India
a r t i c l e
i n f o
Article history: Received 9 May 2019 Received in revised form 15 June 2019 Accepted 17 June 2019 Available online xxxx Keywords: WEDM ANOVA Surface roughness (Ra) MRR Taguchi
a b s t r a c t Wire EDM (WEDM) is a special method of electrical discharge machining. Maraging steel had very-low carbon steel which consists of more material toughness and strength. In present work a Taguchi optimization method proposed for WEDM of maraging steel 350 and the machining output responses are Material Removal Rate (MRR) and Surface roughness (Ra). Machining process is planned by Taguchi’s robust design. The analysis is performed for the results obtained by using analysis of variance (ANOVA). The optimum conditions for this work are performed through Taguchi analysis. Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 2nd International Conference on Applied Sciences and Technology (ICAST-2019): Materials Science.
1. Introduction
2. Literature review
Wire EDM (WEDM), commonly called Electric Discharge Wire Cutting (WDWC) and is a special method of electrical discharge machining used a small wire diameter as the electrode for cutting a small kerf in the work. The cutting method in wire EDM is processed by thermal energy from electric discharge among the electrode wire and the workpiece. The workpiece is fed past the wire to achieve the desired cutting path; somewhat in the manner of a band saw operation. Numerical control is used to control the work part motions during cutting. As it cutting wire gradually and continuously advanced within a supply spool and take-up spool to present a recent electrode with no changes in diameter to the work. This helps to control a constant slot width during cutting. As in Electric discharge machine, wire EDM needs to be carried out in the existence of dielectric flow. This is applying through nozzles controlled at the work material is immersed in a dielectric bath. Wire diameter bounds from 0.0716 to 0.30 mm; depending on prescribed kerf width electrode materials are tungsten, copper, brass, and molybdenum. Work part toughness and Hardness do not issue the cutting performance. The only need is material should be electrically conductive. The schematic diagram of WEDM represented in Fig. 1 (see Table 1).
From the literature reviews for MRR and Ra, some parameters influence very much those parameters such as pulse on time, pulse off time, peak current, spark gap voltage is selected in the present work to analyse the most influential parameters for MRR and Ra. Taguchi design of experiments is the most commonly used method in the experimental procedure and to obtain the mathematical equations and significance of parameters are identified through ANOVA.
⇑ Corresponding author. E-mail address: [email protected] (K. Dayakar).
3. Material selection Maraging steel is a very low carbon content steel that has high material strength and toughness The term ’maraging’ represents for strengthening mechanisms; ’mar’ indicates to martensite and ’aging’ for a heat treatment called age hardening. Maraging steels are hugely sought in applications that need precise and special geometries and demand operation at high loads. Applications are Rocket motor casings, Take-off and landing gear in aircraft, fencing blades, etc (see Table 2). 4. Experimental setup and methodology Experiments are machined on Ultra-cut S1 Wire cut EDM CNC machine, making by Electronica (India). The electrode
https://doi.org/10.1016/j.matpr.2019.06.635 2214-7853/Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 2nd International Conference on Applied Sciences and Technology (ICAST-2019): Materials Science.
Please cite this article as: K. Dayakar, K. V. M. Krishnam Raju and C. Rama Bhadri Raju, Prediction and optimization of surface roughness and MRR in wire EDM of maraging steel 350, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.635
2
K. Dayakar et al. / Materials Today: Proceedings xxx (xxxx) xxx
Fig. 1. Schematic diagram of WEDM.
Table 1 A literature review. S no
Author
Type of material used
Design of experiments used technique
Input process parameters
Output response
Find out from the study
1.
Alagarswamy et al. [1]
AA7075/ B4CP MMC
Pulse on time, pulse off time, spark voltage, wire tension
Surface roughness
Pulse on time is the most significant parameter
2.
Gijoy et al. [2]
SS304
Taguchi L9 orthogonal array Taguchi L9 orthogonal array
Pulse on time, pulse off time, wire feed.
Wire feed is most influenced parameter on MRR; pulse on time is the most influenced parameter on surface roughness.
3.
Mathew et al. [3]
AISI 304
Taguchi L 27
Pulse on time, pulse off time, spark gap voltage, water pressure, wire feed, wire tension
4.
Johnson et al. [4]
AL6061
Taguchi L18
5.
Arun Kumar et al. [5]
Berlium copper alloy
RSM29 experiments
Pulse on time, the pulse of time, feed rate, wire tension. Pulse on time. The pulse off time, spark gap voltage.
6.
AISI 316 SS
Taguchi L27
7
Padmavathi et al. [6] Kumar et al. [7]
Inconel x750
Taguchi L27
8.
Kumar et al. [8]
EN-31
Taguchi L16
Pulse on time, pulse off time, spark gap voltage, wire feed
Surface roughness, the material removal rate Surface roughness and material removal rate Surface roughness Surface roughness and material removal rate. Material removal rate Surface roughness and cutting speed Material removal rate
9.
Kapoor et al. [9]
Taguchi L9
10.
Rao et al. [10]
M-35 HSS 5% cobalt Aluminum alloy
Pulse on time, pulse off time, spark gap voltage, peak current. Pulse on time, pulse off time, flushing pressure, wire feed, wire tension, spark gap voltage, servo feed rate.
Taguchi L18
Pulse on time, pulse off time, peak current, wire tension, wire feed rate. Pulse on time, pulse off time, servo voltage, peak current, wire feed, wire T
Material removal rate Surface roughness and material removal rate
Pulse on time contributes to the material removal rate and surface roughness.
Surface roughness influenced by pulse on time. Pulse on time significances on surface roughness and MRR
Peak current and pulse on time contribute on MRR Pulse on time, the pulse of time, servo voltage and peak current are more contributes to cutting speed and MRR Maximum MRR at pulse on time 120, pulse off time 25, wire feed 11, spark gap voltage 40. Peak current is the most significant parameter Pulse on time, peak current and spark gap voltage are the most significant parameters on surface roughness and MRR
Table 2 Composition of maraging steel 350. Chemical
C
Si
Mn
Ni
Co
Mo
Ti
Al
Fe
%
<0.03%
<0.10%
<0.10%
<18.50%
<12.00%
<4.80%
<1.40%
<0.10%
Reminder
material used as brass with diameter 0.25 mm; demineralized water is a dielectric fluid. Design of experiments is based on Taguchi L27 orthogonal array model. Output response character-
ization is by ANOVA to find the input process parameters that effect on Ra and MRR. After complete of machining, Ra is measured by surface roughness Tester model SJ210. The MRR is
Please cite this article as: K. Dayakar, K. V. M. Krishnam Raju and C. Rama Bhadri Raju, Prediction and optimization of surface roughness and MRR in wire EDM of maraging steel 350, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.635
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Fig. 2. Specimens after machining.
obtained by Eq. (1) and Fig. 2 Shows Experimental specimens after machining. MRR ¼ ð2 Wg þ DÞ t VC mm3 =min
ð1Þ
where ’Wg’ is spark gap (mm), ’D’ is the wire electrode diameter in mm,’t’ is the thickness of workpiece in mm, ’VC’ is cutting speed in mm/min. The cutting speed is displayed on the computer while machining. The size of the workpiece is 10 9 9 in mm. The optimization is performed by the Taguchi method for obtaining the optimal conditions for lower surface roughness and higher material removal rate. Four input process parameters are being selected for experimentation which is given in the Table 3. By using experimental data mathematical models and its corresponding equations and S/N plots are obtained by Minitab software. The following table contents the taking parameters and their levels 5. Design of experiments for surface roughness and material removal rate 5.1. Analysis of variance ANOVA (Analysis of Variance) is an analytical way which describes the factors significantly affecting the experimental results. ANOVA consists of All Experimental Data distribution for summing squares. Unbiased variance Decomposing of the total sum to sums of squares that are considering all elements used in the analysis. Calculates unbiased variances over sums of squares considering all elements over their DOF. calculating the variance ratio by dividing each unbiased variance by the error variance;
Searching which factors significantly affect experimental results by analysing the error variance. This procedure can be polished by design an ANOVA table. 5.2. Taguchi analysis Design of parameter is the process of investigation leading to the establishment of optimal values of the design parameters so that the product/process perform on target and is not influenced by the noise factors. Statistically designed experiments and/or orthogonal experiments are used for this purpose. 5.2.1. 5.2.1Smaller-the better Here, the quality characteristic is non-negative and continues. It can take any value among 0 to 1. The desired value (the target) is zero. These problems are characterized by the scaling factor (ex: surface roughness, pollution, tire wear, etc.). The S/N ratio (ɳ) is given below. Ni X y2k SNi ¼ 10log Ni k¼1
! ð2Þ
5.2.2. Larger-the better The condition characteristic is non-negative and continuous. It can take any Y2i Value from 0 to 1. The ideal target value of this form of quality characteristic is _ (as large as possible). Quality characteristics like strength values, fuel efficiency, etc. are examples of this type. In these problems, there is no scaling factor. The S/N ratio (ɳ) is given below
SNi ¼ 10log
1X 1 n Y 2i
! ð3Þ
Table 3 Process parameters and their levels. S no
Parameters
Units
Symbol
Level 1
Level 2
Level 3
1 2 3 4
Pulse on time (TON) Pulse off time (TOFF) Peak current (IP) Spark gap voltage (SV)
Micro seconds Microseconds Ampere Volts
l sec l sec
100 55 10 20
108 59 11 50
108 63 12 80
A V
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Table 4 Input parameter values for Ra and MRR experimental and predicted results. S no
T on
T off
IP
SV
Ra
Predicted Ra
MRR
Predicted MRR
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
100 100 100 100 100 100 100 100 100 104 104 104 104 104 104 104 104 104 108 108 108 108 108 108 108 108 108
55 55 55 59 59 59 63 63 63 55 55 55 59 59 59 63 63 63 55 55 55 59 59 59 63 63 63
10 11 12 10 11 12 10 11 12 10 11 12 10 11 12 10 11 12 10 11 12 10 11 12 10 11 12
20 50 80 50 80 20 80 20 50 20 50 80 50 80 20 80 20 50 20 50 80 50 80 20 80 20 50
3.895 1.982 1.276 2.669 1.809 1.463 3.47 2.01 1.424 3.349 2.757 2.974 3.046 2.484 2.798 3.724 2.574 1.748 3.361 1.684 1.678 2.862 2.177 1.475 3.716 3.269 1.853
3.301 1.799 1.713 2.596 1.923 1.57 3.295 2.355 1.441 3.907 2.406 2.32 3.203 2.529 2.177 3.901 2.961 2.047 3.532 2.03 1.944 2.827 2.153 1.801 3.526 2.586 1.672
0.516 2.012 2.227 0.580 1.702 1.660 0.648 1.365 1.509 0.590 2.012 2.835 0.541 2.025 2.029 0.445 1.660 2.051 0.516 2.128 2.349 0.657 1.822 2.029 0.405 1.476 2.012
0.483 1.923 2.307 0.429 1.791 1.778 0.371 1.337 1.799 0.701 2.142 2.525 0.648 2.01 1.997 0.59 1.555 2.018 0.613 2.054 2.437 0.56 1.922 1.909 0.502 1.467 1.929
Ra ¼ 2:5010 0:279 T on 100 þ 0:327 T on 104
6. Results
0:048 T on 108 þ 0:050 T off 55
6.1. Experimental results
0:192 T off 59 þ 0:142 T off 63 þ 0:843 IP 10
The following Table 4 consist of given input process parameters and their levels and experimental results and predicted results.
0:196 IP 11 0:647 IP 12 þ 0:187 sv 20 0:276 sv 50 þ 0:089 sv 80
ð4Þ
6.2. Analysis of variance 6.2.1. For surface roughness The following Table 5 constructed from experimental data by using analytical software for surface roughness. From the ANOVA Table 5 for surface roughness (Ra), mainly influenced on peak current (IP), which contributes 61.91%, and for a pulse on time (Ton) which it contributes 9.94%. Pulse off time (T off) contributes 3.15%; spark gap voltage (SV) contributes 6.32% with an error of 18.68%. Regression equation:
Table 7 Response table for signal to noise ratios. Level
T on
T off
IP
SV
1 2 3 Delta Rank
6.310 8.864 7.318 2.553 2
7.577 6.974 7.941 0.968 4
10.422 7.072 4.997 5.425 1
8.113 6.655 7.724 1.459 3
Table 5 ANOVA table for Ra. Source
DF
Seq SS
Contribution
Adj SS
Adj MS
F-Value
P-Value
T on T off IP SV Error Total
2 2 2 2 18 26
1.6852 0.5350 10.4981 1.0720 3.1679 16.9580
9.94% 3.15% 61.91% 6.32% 18.68% 100.00%
1.6852 0.5350 10.4981 1.0720 3.1679
0.8426 0.2675 5.2490 0.5360 0.1760
4.79 1.52 29.83 3.05
0.022 0.246 0.000 0.073
Table 6 ANOVA table for MRR. Source
DF
Seq SS
Contribution
Adj SS
Adj MS
F-Value
P-Value
T on T off IP SV Error Total
2 2 2 2 18 26
0.2181 0.7343 12.0174 0.3891 0.4649 13.8238
1.58% 5.31% 86.93% 2.81% 3.36% 100.00%
0.2181 0.7343 12.0174 0.3891 0.4649
0.10905 0.36714 6.00869 0.19454 0.02583
4.22 14.21 232.64 7.53
0.031 0.000 0.000 0.004
Please cite this article as: K. Dayakar, K. V. M. Krishnam Raju and C. Rama Bhadri Raju, Prediction and optimization of surface roughness and MRR in wire EDM of maraging steel 350, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.635
K. Dayakar et al. / Materials Today: Proceedings xxx (xxxx) xxx Table 8 Response table for signal to noise ratios. Level
T on
T off
IP
SV
1 2 3 Delta Rank
1.5673 2.3470 1.9524 0.7797 4
2.8982 2.1086 0.8598 2.0385 2
5.3753 5.0180 6.2239 11.5991 1
1.2378 2.3240 2.3049 1.0861 3
5
6.2.2. For material removal rate The following Table 6 constructed from experimental data by using analytical software for Material removal rate. From the ANOVA table for MRR, highly influenced on peak current (IP) which contributes 86.93%, and for pulse off time (T off) contributes 5.31%. Spark gap voltage (SV) contributes 3.36% and pulse on time (Ton) contributes 1.58% with error 3.36%. Regression equation (see Table 7):
Fig. 3. Variation of Surface Roughness with other parameters.
Fig. 4. Variation of SN ratios with Surface Roughness.
Please cite this article as: K. Dayakar, K. V. M. Krishnam Raju and C. Rama Bhadri Raju, Prediction and optimization of surface roughness and MRR in wire EDM of maraging steel 350, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.635
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Fig. 5. Variation of MRR with other parameters.
Fig. 6. Variations for SN ratios with MRR.
MRR ¼ 1:4745 0:1165 T on 100 þ 0:1023 T on 104
6.3. Taguchi analysis
þ 0:0141 T on 108 þ 0:2131T off 55 0:0246 T off 59 0:1886 T off 63 0:9298 IP 10 þ 0:3260 IP 11 þ 0:6037 IP 12 0:1584 sv 20 þ 0:0262 sv 50 þ 0:1321 sv 80
ð5Þ
6.3.1. For surface roughness From the response Table 8, SN ratios for Ra Taguchi analysis calculated by smaller is better Eq. (2) the ranking is given by Minitab software 18. The ranks are peak current (IP) 1, pulse on time (Ton) 2, spark gap voltage (SV) 3, pulse off time (T off) 4.
Please cite this article as: K. Dayakar, K. V. M. Krishnam Raju and C. Rama Bhadri Raju, Prediction and optimization of surface roughness and MRR in wire EDM of maraging steel 350, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.635
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Fig. 7. Interaction plot for MRR.
Fig. 8. Interaction plot for surface roughness.
Please cite this article as: K. Dayakar, K. V. M. Krishnam Raju and C. Rama Bhadri Raju, Prediction and optimization of surface roughness and MRR in wire EDM of maraging steel 350, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.635
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Fig. 9. For Experimental Ra vs. Predicted Ra.
Fig. 10. For Experimental MRR vs. Predicted MRR.
6.3.2. For material removal rate From SN ratio response table for surface roughness Taguchi analysis calculated by larger is better Eq. (3) the ranking is given by Minitab software 18. The ranks are peak current (IP) 1, pulse off time (T off) 2, spark gap voltage (SV) 3, pulse on time (Ton) 4. Fig. 3 shows the variation of surface roughness in each input process parameters and Fig. 4 shows the optimum values for surface roughness in each input process parameter. Fig. 5 shows the variation of MRR in each input process parameters and Fig. 6 shows the optimum values for MRR in each input process parameter. Fig. 7 shows the intractions in each input process parameter for surace roughness, Fig. 8 shows intractions in each input parameters for MRR. Fig. 9 indicates the comparison graph from experimental data and predicted data for surface roughness and Fig. 10 shows comparison graph from experimental data and predicted data for MRR.
7. Conclusions This work is performed by using design of experiments on maraging steel 350 which is having high strength and toughness compared to other materials. This paper mainly focused in Ra and MRR by varying input process parameters pulse on time, pulse off time, peak current and spark gap voltage. The following are the conclusions drawn from this work.
Surface roughness increases when pulse on time and peak current increases. The Optimum conditions for surface roughness (Ra) are obtained at pulse on time 100 lsec, pulse off time 59 lsec, peak current 12 A and spark gap voltage 50 V. Peak current is the highest influenced parameter on surface roughness. MRR increases when Pulse on time, spark gap voltage and peak current increases. The Optimum conditions for MRR are obtained at pulse on time 104 lsec, pulse off time 55 lsec, peak current12 A and spark gap voltage 50 V. Peak current is the highest influenced parameter on MRR. References [1] S.V. Alagarsamy, M. Ravichandran, S. Vignesh, S.A.V. Sagayaraj, Multiperformance optimization of wire cut EDM process parameters on surface roughness of AA7075/B4Cp metal matrix composites, Magnesium, 2, 2–9. [2] S. Gijoy, S.S. Abhilash, S. Hari Krishnan, Optimization of wire electrical discharge machining process parameters using Taguchi method, Int. J. Curr. Eng. Sci. Res. (IJCESR) 4 (7) (2017). [3] Bijo Mathew, B.A. Benkim, J. Babu, Multiple process parameter optimization of WEDM on AISI304 using utility approach, Procedia Mater. Sci. 5 (2014) 1863– 1872. [4] Johnson Jerin, K.T. Bibin, Anoop Sankar, Optimization of wire electric discharge machining parameters on Al 6061, Int. J. Eng. Sci. Res. Technol. (2018). [5] N.E. Arun Kumar, Suresh Babu, Murali, A study on parametric optimization of wire electrical discharge machining using response surface methodology, t 1051-1059issn 0972-768x J. Chem. Sci. 14 (2) (2016).
Please cite this article as: K. Dayakar, K. V. M. Krishnam Raju and C. Rama Bhadri Raju, Prediction and optimization of surface roughness and MRR in wire EDM of maraging steel 350, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.635
K. Dayakar et al. / Materials Today: Proceedings xxx (xxxx) xxx [6] K.R. Padmavathi, S. Devaraj, I. John Solomon, A. Premkumar, Influence of process parameters on wire EDM process for AISI 316 stainless steel, Int. J. Eng. Res. Technol. (IJERT) 6 (02) (2018), Special Issue. [7] M. Kumar, H. Singh, Multi response optimization in wire electrical discharge machining of Inconel X-750 using Taguchi’s technique and grey relational analysis, Cogent Eng. 3 (1) (2016) 1266123. [8] Surinder Kumar, Parveen Kumar, Investigation of material removal rate for wire-cut EDM of EN-31 alloy steel using Taguchi technique, Int. J. Sci. Eng. Res. 7 (12) (2016).
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[9] Shrey Kapoor, Sharad Shrivastava, Optimisation and influence of process parameters for machining with wire cut EDM using Taguchi’s technique, Int. J. Adv. Res. Sci. Eng. Technol. 5 (6) (June 2018). [10] P.S. Rao, K. Ramji, B. Satyanarayana, Experimental investigation and optimization of wire EDM parameters for surface roughness, MRR and white layer in machining of aluminium alloy, Procedia Mater. Sci. 5 (2014) 2197–2206.
Please cite this article as: K. Dayakar, K. V. M. Krishnam Raju and C. Rama Bhadri Raju, Prediction and optimization of surface roughness and MRR in wire EDM of maraging steel 350, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.635