TÀI LIỆU THAM KHẢO TIENG VIET
SAN PHAM KHOA HỌC
Effect of Input Factors on Surface Roughness when EDM SS304 Cylindrical Shaped Parts
—— TranNgoc Giang), Nguyen Hong Linh”, Tran Ngoc Huy Thinh, Le Hoang Anh’, Nguyen Huu
Quang”, Tran Thanh Hoang”, Nguyen Manh Cuong’, Nguyen Anh Tuan® = Thai Nguyen University of Technology, Vietnam!®”
. Electric Power University, Vietnam?
| Nguyen Tat Thanh University, Vietnam*
; - Vinh Long University of Technology Education, Vietnam“
University of Economics - Technology for Industries, Vietnam°Š
*Email: natuan.ck@uneti.edu.vn
Keywords: ABSTRACT
EDM, In this paper, a study on optimizing electrical discharge machining
oe (EDM) when processing cylindrical shaped parts of stainless steel SS304__
Stainless steel, — Cylindrical shaped parts, __ to achieve minimum surface roughness (SR) was introduced. To solve
To V0 v0) = = optimization problem, an experiment was designed and performed.
Surface roughness. Besides, the Taguchi method in the Minitab R19 software was used to design the experiment and analyze the results. In addition, the influence of the input process parameters, including the pulse on time, the pulse off time, the servo current, and the servo voltage on SR was investigated.
Furthermore, the optimal input factors of the EDM process for minimum SR were found.
This work is licensed under a Creative Commons Attribution Non-Commercial 4.0 International License,
1. TNTRODUCTION
Electrical discharge machining (EDM) is a non-traditional machining method that uses sparks generated between the electrode and the workpiece to remove the workpiece material. This method is very widely used to process conductive parts that are difficult to machine. It is especially effective in processing dies, molds, etc. Therefore, there have been many studies on the EDM process.
Up to now, studies on EDM process have been conducted for different types of EDM such as die sinking EDM [1-3], micro-EDM drilling [4-6], or Wire EDM [7-9]. Many studies have focused on solutions to improve the efficiency of the EDM process such as Powder Mixed Electrical Discharge Machining (PMEDM) [10-14], or vibration assisted EDM [15, 16]. Recently, a number of studies have applied this type of machining to process cylindrical shaped parts. Recently, a number of studies have applied this type of machining to process cylindrical shaped parts [17, 18].
However, so far there has been no research when EDM S$304 stainless steel.
This paper introduces a study on optimizing the EDM process when machining cylindrical shaped parts with the work-material of SS304 stainless steel, In the study, the influence of the input process parameters on SR was evaluated. In addition, optimal input factors to achieve minimum surface roughness were proposed.
2, EXPERIMENTAL SETUP
To evaluate the impact of the input parameters on SR when machining SS304 cylindrical shaped
SỐ
Pe TABLE 1, Input parameters and their levels — a Level
No. Input factors Code Unit 2 3
|
1 Pulse on time Ton hs 6 10 14
2 Pulse offtime = Toff Ls 14 271) |
3 Servo current IP A 6 Ộ) 12
4 Servovoltage. SV Vv 4 5 6
_ _ TABLE 2. Experimental plan, R, values, and S/N ratios
Exp. Input Parameters R, (mm)
No Ton Toff Ip SV ‘riall Trai2 Trai Mean of
- 7 —ˆ J5 14 ae 4 2.348. 2,341 2307: 2.36200 -7.4661. =
2 6 21 2 5——2,220-—_—2.422- 2,037 212633 76,5500 =”1.-
a 6 30 12 6 2.360 2.415 2.300, 2.38833 -7.5623
4 10 14 9 6 2.618 2.649 2.573 2.61333 -8.3445
5 10 21 12 4 3.474 3.346 3.594 3.47133 -10.8136
6 10 30 6 5 3.580 3.661 3.469 3.57000 -11.0555
a 14 14 12 5 4.194 4.290 4.733 4.40567 212.0020906 9
` 8 14 21 6 6 4.585 4.439 4712 4.57867 -13.2174
= 9 14 30 9 4 2.785 2.687 2.610 2.69400 -8.6110
parts. Also, four input parameters, including the pulse on time, the pulse off tỉme,:the servo current, and the servo voltage were selected for the experiment. Table 1 shows these parameters with the selected levels. Besides, the Taguchi method in the Minitab R19 software was used to design the experiment and analyze the results. The design L9 (34) was used for the experiment so 9 experimental runs will be performed. Figure 1 shows the experimental setup in which a Sodick A30 EDM machine, $$304 stainless steel samples, copper electrodes, and Diel MS 7000 dielectric
___ fluid were used. Table 2 describes the test plan and SR's values. — - =
Fig. 1. Experimental setup 3. RESUTL AND ANALYSIS
In this study, the analysis of variance (ANOVA) method was used to evaluate the influénce of the input EDM parameters on SR. With the objective function is the minimum surface roughness is
: Electrode
ễ Workpiece
best, the condition is required:
$1w==l0bsu = 309 (1)
nia
ANOVA results (table 3) show that T,, has the greatest effect on Ry (58.35%); followed by the influence of IP (29.36%), SV (6.46%), and Tyr (5.83%). The impact of these parameters (%) on R, is also clearly shown in the chart in Figure 2.
The order of influence of the input parameters on R, is described in Table 4. From this table, it can be seen that the effect of the input parameters (%).on Ry is Ton, IP, SV and Toff, respectively.
TABLE 3. ANOVA for Means Analysis of Variance for Means
Source DF SeqSS AdjSS AdjMS F P C(%) Ton 2 3.87428 3.87428 1.93714 * * 58,35 Toff 2 038734 0438734 019367 * * 5.83 IP 2 194961 194961 0.97480 * * 2936
=—~x + SV 2 0442895 042895. 021447 * + 646 — TT Residual Error eB + = £
= ~~ Total ——8 664017
uTon 8Toff =IP asv
Fig. 2. Effect of input factors on R, (%) TABLE 4. Order of influence of input factors on R,
Response Table for Means
Level Ton Toff IP SV 1 2.292 3.127 3.504 2.842 2 3.218 3.392 2.478 3.367 3 3.893 2.884 3.422 3.193 Delta 1.601 0.508 1.026 0.525 Rank 1 4 2 3
However, in order to determine the optimal set of parameters for the minimum surface texture, it is necessary to consider the S/N ratio of Ra (Figure 3). From Figure 3, it can be seen that as alien ___ increases, the S/N value decreases. When Toi increases from level 1 to level 2, S/N decreases;
however, when increasing T,¢ from level 2 to level 3, S/N increases. When IP increases from level 1 to level 2 makes S/N increase but when it increases from level 2 to level 3 S/N decreases, The effect of SV is as follows: when SV increases from level 1 to level 2, S/N decreases. But if further increase from level 2 to level 3, S/N will only increase slightly. From Figure 3, it is easy to determine the optimal input parameters for the minimum Ra value (Table 5).
D5)
Main Effects Plot for SN ratios
Data Means
- Ton i Toff. se edd ele ị .
© |
| 8
+8 i / \
3 i i
số “+ :
= : ` an ị vs
Š +410 ` Ị : | \ `
. °
i °
an |
` ị
i ——— | =“ 2
6 10 1 14 a 30 6 9 12 4 5 6
——= _—— Signal-to-noise: Smaller is better = = a =. — a
Fig. 3. Influence of input factors on the S/N of Ry TABLE 5. Optimum input factors for minimum R,
No. Input parameters Code Unit Level Value
1 Pulseontime Tế Ms 1 6
2 Pulseofftime Tore hs 3 30
3 Peak current T A 2 9
4 Servo voltage SV V 1 4
The value of Ra corresponding to the optimal input parameter values is calculated based on the prediction method using Minitab 19 software. The calculated Ry is 2.01609 (tum) (Table 6).
TABLE 6. Calculated R, based on optimum parameters
Settings
Variable _ Setting
Ton 6
Toff 30 IP 9)
s⁄—— 4
Prediction
Fit SEFit 95% Cl 95% PI
2.01609 0.623393 (0.285273, 3.74691) (-0.775659, 4.80784)
Figure 4 shows the Normal Probability Plot for Mean, It was noted that the errors of the experimental points (the blue points) are close to the normal distribution line (red solid line), It shows that the error level is very small.
The Anderson-Darling graph (Figure 5) is used to estimate the suitability of the experimental model with the optimal process parameters. It is found that the experimental data (blue dots) are in the limit region with 95% significance level. Moreover, the P value of 0.181 is greater than the value of ô = 0.05. That means the applied model is dependable with the above significance level,
56
Normal Probability Plot
SE)
° ~
90 4
_e
Đ 50 ve
o ae
i 4
10 6. °
1 ‘ : sa
-1.0 0.5 0.0 0.5 1.0
Residual
Fig. 4. Normal Probability Plot for Mean
Probability Plot of MEAN
— Mormai-95%C| — — = —
a Ỷ 7 4 | Men 3434
| Sí ⁄ | §IDev 0/9211 / Z : “ i N 9)
95 | / hn fe 2 SE — ÊNaus Ghi |
90. 7 i |
tu oy |
70: % 7” |
© 60) ° |
Ð sọ) fo |
@ 40: Je |
30 aay :
20 é |
Ti a |
i *
sị
al Ề _ = : =
0 1 2 3 4 5 6 7
MEAN
Fig. 5. Probability Plot for Mean 4. CONCLUSION
This paper deals with the results of an optimization study on EDM process when machining SS304 cylindrical shaped parts for minimum SR. In the study, the effect of the input factors, including the pulse on time, the pulse off time, the servo current, and the servo voltage were investigated. It was reported that T,, is the most effected factor on R, (58.35%); followed by IP (29.36%), SV (6.46%), and Ty (5.83%). Finally, optimal input factors to get minimum SR were proposed.
5. ACKNOWLEDGMENT
This work was supported by Thai Nguyen University of Technology.
6. REFERENCES
1, Joshi, S. and S. Pande, Thermo-physical modeling of die-sinking EDM process. Journal of manufacturing processes, 2010. 12(1): p. 45-56.
2. Salonitis, K., et al., Thermal modeling of the material removal rate and surface roughness Jor die-sinking EDM. The International Journal of Advanced Manufacturing Technology,
2009. 40(3): p. 316-323.
3: Liu, Y., et al., Investigation of emulsion for die sinking EDM. The International Journal of Advanced Manufacturing Technology, 2010. 47(1): p. 403-409.
4. Diver, C., et al., Micro-EDM drilling of tapered holes for industrial applications. Journal of Materials Processing Technology, 2004. 149(1-3): p. 296-303.
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a;
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l6 14.
St
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18.
Plaza, S., et al., Experimental study on micro EDM-drilling of Ti6Al4V using helical electrode, Precision Engineering, 2014. 38(4): p. 821-827.
Ay, M., U. Caydas, and A. Hasgalik, Optimization of micro-EDM drilling of Inconel 718 superalloy. The International Journal of Advanced Manufacturing Technology, 2013. 66(5-
8): p. 1015-1023,
Spedding, T.A. and Z. Wang, Study on modeling of wire EDM process. Journal of Materials Processing Technology, 1997. 69(1-3): p. 18-28.
Tomura, S. and M. Kunieda, Analysis of electromagnetic force in wire-EDM. Precision engineering, 2009. 33(3): p. 255-262.
Newton, T.R., et al., Jnvestigation of the effect of process parameters on the formation and characteristics of recast layer in wire-EDM of Inconel 718. Materials Science and
Engineering: A, 2009. 513: p. 208-215.
Hong, T.T., et al. Effect of Process Parameters on Machining Time in PMEDM Cylindrical Shaped Parts with Silicon Carbide Powder Suspended Dielectric. in Materials Science Forum, 2021, Trans Tech Publ.
Hong, T.T., et al. Multi-Objective Optimization of PMEDM Process of 90CrSi Alloy Steel Jor Minimum Electrode Wear Rate and Maximum Material Removal Rate with Silicon
Carbide Powder. in Materials Science Forum. 2021. Trans Tech Publ, = Hong, T.T., et al. A Study on Influence of Input Parameters on Surface Roughness in PMEDM Cylindrical Shaped Parts, in Materials Science Forum. 2021, Trans Tech Publ.
Hong, T.T., et al. Multi-Objective Optimization of PMEDM input Factors Jor Processing Cylindrical Shaped Parts. in Materials Science Forum. 2021. Trans Tech Publ.
Nguyen, M.C., et al., Influence of input factors on material removal rate in PMEDM cylindrical shaped parts with silicon carbide powder suspended dielectric. Key Engineering Materials, 2020. 861: p. 129.
Endo, T., T. Tsujimoto, and K. Mitsui, Study of vibration-assisted micro-EDM—the effect of vibration on machining time and stability of discharge. Precision Engineering, 2008.
32(4): p. 269-277.
Sabyrov, N., et al., Ultrasonic vibration assisted electro-discharge machining (edm)—An overview, Materials, 2019, 12(3): p. 522.
Ky, L.H., et al. Multi-Objective Optimization of Surface Roughness and Electrode Wear in EDM Cylindrical Shaped Parts. in Materials Science Forum. 2021. Trans Tech Publ.
Hong, T.T., et al. Effects of Input Parameters on Electrode Wear Rate when EDM Cylindrical Shaped Parts. in Materials Science Forum. 2021. Trans Tech Publ.
58
ISSN: 00845841
TA EE pened) Volume XX, Issue XX, December, XXXX
Influence of EDM Parameters on Material Removal
Speed when Processing SS304 Cylindrical Shaped Parts
Nguyen Hong Linh', Nguyen Thanh Tw’, Tran Quoc Hoang’, Le Hoang Anh‘, Nguyen Anh Tuan’, Tran — -
Thanh Hoang”, Nguyen Manh Cuong’, Trinh Kieu Tuan*”
Electric Power University, Vietnam!
Thai Nguyen University of Technology, Vietnam?”
___ Nguyen Tat Thanh University, Vietnam?
Vinh Long University of Technology Education, Vietnam’
University of Economics - Technology for Industries, Vietnam™®
*Email: tktuan@uneti.edu.vn
CrossMark
Stainless steel, — Cylindrical shaped parts,
Keywords: ABSTRACT
EDM, This article presents the results of a study on optimization of electrical SS304, ơ _discharge_machining_(BDM)_whenmachiningSS304-stainless steel———
cylindrical shaped parts, The objective function of the problem is the
: maximum material removal speed (MRS). An experiment has been
Material removal speed. designed and performed to solve this problem. In addition, the
experimental design and result's analysis were carried out by applying the Taguchi method with the support of the Minitab R19 software.
Besides, the influence of the input process parameters, including the pulse on time, the pulse off time, the servo current, and the servo voltage on the MRS was explored. Furthermore, the optimal inputs of the EDM process factors to achieve the maximum MRS have been proposed.
This work is-licensed-under a Creative Commons Attribution Non-Commercial 4,0 International License.
1, INTRODUCTION
Electrical discharge machining (EDM) is one of the most used non-traditional machining methods in industries. This method removes the workpiece by means of electrical sparks generated between the electrode and the workpiece which is a conductive material. The EDM process is very commonly used to create recesses such as dies, molds, etc. Therefore, the study of the EDM process has become attractive to many scientists.
Up to now, there have been many studies on the EDM process. Studies have been done with different work materials such as H11 die steel [1-3], SDK11 [4, 5], 90CrSi [6], Ti-6Al-4V [7], etc... The quality of the machined surface has been investigated by many studies [6, 8, 9]. There are several works which have been conducted for rough [10], and finishing [11-13] EDM processes. EDM with support of vibration has been proposed to improve its efficiency [14, 15]. Besides, mixing conductive powder into the dielectric solution is also a method to improve the efficiency of this type of machining [16-18]. Recently, there have been a number of studies on processing cylindrical shaped parts by EDM [19-25]. However, there is no research for SS304 stainless steel.
This paper presents the results of an optimization study on EDM processing cylindrical parts made of SS304 stainless steel for getting maximum MRS, In the study, the effect of the input process parameters on MRS was investigated. In addition, the optimal EDM input factors to achieve the maximum MRS were
found.
Effendi,et.al,XXXX Agricultural Mechanization in Asia 2. EXPERIMENTAL SETUP
To investigate the influence of input parameters on MRS, four input parameters were selected. The input factors with selected levels were described in Table 1. The Taguchi method with L9 design (34) was used
with the assistance of the Minitab R19 software. The test setup is described in Figure 1. In this setup, a.
__ Sodick A30 EDM machine, $S304 stainless steel samples, copper electrodes, and Diel MS 7000 dielectric Ì
fluid were used. The experimental plan, and MRS values were given in Table 2.
TABLE 1. Input factors and their levels
No. Input factors Code fr 4 Unit 7 = pe
: T Pulse on time z= Ton ps 6 10 14
2 Pulse off time Toff Ls 14 21 30
—— 3 Servocurrent. E==. na. mm... 9 — ——
4 Servo voltage SV Vv 4 5 6
TABLE 2. Experimental plan, MRS, and S/N ratios
Exp. Input Parameters MRS (g/h) = SN `.-
No Ton Toff. Ip— SV——_Trialt Trial2 —— Trial3 5
1 lử= 14 6 4 0.9759 0.9919 0.9897 0.98585 -0,1245 Ƒ
2 6 2| 9 5 0.2012 0.1946 0.1933 0.19633 -141441
3 6 30 12 6 5.4850 5.3847 5.3649 5.41155 14.6652
10 14 9) 6 1.4010 1.3801 1.3892 1.39013 2.8606
5 10 21 12 4 2.1104 2.0015 1.9885 2.03345 6.1555
5 3 6 10 30 6 5 5.2794 5.5015 54876 5.42284 146799
7 14 14 12 5 42818 — 4.3148 4.3669 4432114 12.7111
8 14 21 6 6 2.3342 2.2967 2.3046 2.31185 7.2786
9 14 30 9 4 1.8944 1.8744 1.8819 1.88356 5.4993
3. RESUTL-AND ANALYSIS
Electrode
Fig. 1. Experimental setup
Workpiece
To evaluate the effect of the input parameters on MRS, the analysis of variance (ANOVA) method was 2
1M... : : ISSN: 00845841
aus EEL NEE EEE) Volume XX, Issue XX, December, XXKX
used. As MRS is as large as better, the following condition is needed:
== Sy 141 1
S/N 101054 ( 235-5) (1)
ANOVA results (table 3) show that Tạ has the greatest influence on MRS (40.49%); followed by the influence of IP (39.71%), SV (16.46%), and Ty, (3.34%). The degree of influence (%) of the input factors is better visualized through the graph in Figure 2.
Table 4 shows the order of effect of the input parameters on MRS. It can be seen from the table that the
influence of the input factors (%) on MRS is IP, Tog SVm and T,,.
TABLE 3. ANOVA for Means
=1 Analysis of Variance for Means
=—= Source —— DF SeqSS AdjSS AdjMS Ƒ P €(%)
Ton 2 09867 09867 049337 * * 3,34 Toff 2 11.9721 11.9721 5.98604 * * 40,49 IP 2 117410 117410 5.87051 * * 39,71 SV 2 48655 48655 243273 * * 1646
L Residual Error 0 si * k s
Total 8 29,5653 - =a
[w] [10%
Ton WToff wip wsv
Fig. 2. Effect of input parameters on MRS (%) TABLE 4. Order of effect of input parameters on MRS
Response Table for Means
Level Ton Toff IP SV
ee eee
1 2,198 2.232 2.907 1.634 2 2.949 1.514 1.157 3.313
= 3 2839 4239 3.922 3,038
Delta 0.751 2.725 2,765 1.679
Rank 4 2 1 3
Figure 3 shows the influence of the input parameters on the S/N ratio. From this figure, it can be seen that the influence of the input parameters on S/N is as follows: When Ton increases from 6 to 14 s, the S/N increases, Besides, when Toff increases from 14 to 21 us, S/N decreases. However, as it continued to increase from 21 to 30 us, the S/N increased again. The effect of IP on S/N is as follows: an increase of IP from 6 to 9A will decrease S/N, but if it continues to increase from 9 to 12A, S/N will increase. When SV increases from 4 to 6 V, S/N increases, From F igure 3, the optimal input parameters for the maximal MRS can be found (Table 5). From optimal input parameers, the maximal MRS was found by the prediction method using the Minitab 19 software. The calculated MRS was 5.30592 (g/h) (Table 6) with a confidence 3
Effendi,et.al,XXXX Agricultural Mechanization in Asia
level of 95%,
Main Effects Plot for SN ratios
Data Means
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12" { ị
, eo.
j | |
9 | ; | |
1 —° | *ị
Zz ° \ fester in|
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2 Ve) ae :
š z \ i | ị
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i i ị
===.ố ==.. = = 6 + = = = ——= = = =a
ae Se =e ee et a
6 10 6 77809016 30 6 9 là 4 5 6
Signal-to-noise: Larger is better
"- Fig. 3. Influence of input parameters on S/N of MRS TABLE 5. Optimum input parameters for maximal MRS
No. Input parameters Code Unit Level Value
1 Pulse on time Ata ls 3 14
2 Pulse off time Torr us 3 30
3 Peak current ip A 3 12
4 Servo voltage SV V 3 6
| = TABLE 6. Calculated MRS based on optimum input factors
Settings
Variable Setting
Ton 14
Toff 30
IP 12
SV : 6
Prediction
Fit. SE Fit 95% Cl 95% PI
5.30592 1,86282 (0.133895, 10.4779) (-2.47655, 13.0884)
The Normal Probability Plot for Mean is shown in Figure 4. From the figure, the errors of the experimental points (the blue points) are very close to the normal distribution line (the red solid line). That means the error level is very small.
, To evaluate the fit of the experimental model with the optimal process parameters, Anderson-Darling graph was plotted on Figure 5. From this graph, it is easy to see that the experimental data (the blue dots) is in the limit region at the 95% significance level. In addition, the P value of 0.191 is greater than the value of œ = 0.05. Thus, the applied model is reliable with the above significance level.
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AUS Meee 2 Volume XX, Issue XX, December, XXXX
Normal Probability Plot
99
a
90 —*
# —* °
oO ÿ 50 v.v” we
a ®“ :
, SẼ
10) F số
12 7 S
4 2 0 2 4
Residual
se a Fig: 4. Normal Probability Plot————
Probability Plot of MEAN Normal ô 95% c|
ir | : 7 iy, 7 7 Z| Mean. 2.662 Swev 1922
| 7 ee Wi AD 0461
= 9 # ` a i Pevatue 6491
904 ⁄ ị
at yoy |
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⁄ “ ⁄ |
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MEAN
Fig. 5. Probability Plot for Mean 4. CONCLUSION
This paper presents the results of an optimization study on EDM process when processing SS304 cylindrical parts for getting maximum MRS. In the study, the influence of the input factors, including the pulse on time, the pulse off time, the servo current, and the servo voltage on the MRS was evaluated.
Accordingly, To has the greatest influence on MRS (40.49%); followed by the influence of IP (39.71%), SV (16.46%), and T,, (3.34%). In addition, the optimal input factors to obtain the maximum MRS have been given.
5. ACKNOWLEDGMENT
This work was supported by Thai Nguyen University of Technology.
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