DOI : 10.17577/IJERTCONV4IS31005
- Open Access
- Total Downloads : 21
- Authors : Rudreshi Addamani , H V Ravindra , Darshan C S , Praveen Kumar
- Paper ID : IJERTCONV4IS31005
- Volume & Issue : ETMET – 2016 (Volume 4 – Issue 31)
- Published (First Online): 24-04-2018
- ISSN (Online) : 2278-0181
- Publisher Name : IJERT
- License:
This work is licensed under a Creative Commons Attribution 4.0 International License
Optimization of P-GMAW Parameters for ASTM A106 Pipes using Taguchi Technique
Rudreshi Addamani , H V Ravindra , Darshan C S , Praveen Kumar
Department of Mechanical Engineering,
P.E.S College of Engineering, Mandya, India.
Abstract Parametric optimization of Pulsed Gas Metal Arc Welding (P-GMAW) for Ultimate Tensile Strength and Percentage of Elongation has been performed by using Taguchi method. Welding Current, Gas Flow Rate and Wire Feed Rate were chosen as welding parameters. The material used for this purpose was ASTM A106 pipes having dimensions with outer diameter 25 mm length 300 mm having wall thickness of 3 mm. Argon (85%) and Co2 (15%) was used as a shielding gas. Mild steel filler wire with copper coated of diameter 1.2 mm was used. An orthogonal array, L27 was used to conduct the experiments. Signal to Noise (S/N) ratio and Analysis of Variance (ANOVA) were employed to study the welding characteristics of material. Optimization of parameters was done by Taguchi method using statistical software MINITAB-16. Confirmation tests were carried out with optimal levels of welding parameters to validate the Taguchis optimization method.
KeywordsPulsed Gas, ASTM A106, Taguchi Technique, ANOVA.
I. INTRODUCTION
Pulsed Gas Metal Inert Gas Welding is a variant of Gas Metal Arc welding. Pulsed Gas Metal Inert Gas Welding (P- GMAW) can be used as a fast-follow process at high travel speeds, or it can be run as a high deposition rate, fast-fill process. A variation of the spray transfer mode, pulse-spray is based on the principles of spray transfer but uses a pulsing current to melt the filler wire and allow one small molten droplet to fall with each pulse. It applies waveform control logic (Fig.1) to produce a very precise control of the arc through a broad wire feed speed range. In this process, welding current varies between high and low current levels at regular time interval. This variation in welding current between high and low level is called pulsation of welding current. High current is used for melting of faying surfaces of base metal while low current is used for maintenance of welding arc and allows time for solidification of the weld pool by dissipating of heat to base metal. This feature of current pulsating reduces net heat input to the base metal, so decreases undesirable effects of comparatively high heat input in MIG welding. Selection of the pulse peak current duration depends on the weld pool size and penetration required for welding the work piece a fine grained structure can be achieved using both low and high pulse frequencies.
Pulsed Gas Metal Arc Welding is commonly used for root pass welding of tubes and pipe welding.
The main setting parameters which influence weld quality or wire melting are background current Ib, peak current Ip, background time Tb, and peak time Tp..
Fig. 1. Metal transfer phenomenon
-
LITERATURE REVIEW
S.V. Sapakal & M.T. Telsang presented a research on the optimization of MIG welding parameters using Taguchi design method. In their research they considered welding current, welding voltage and welding speed as input variables and penetration depth as output variable. MS C20 was selected as work piece material. A plan of experiments based on Taguchi technique has been used to acquire the data. An orthogonal array, signal to noise(S/N) ratio and analysis of variance (ANOVA) were employed to investigate the welding characteristics of MS C20 material and optimize the welding parameters. Their experimentation results that the lower current. [1]
S.R. Meshram & N.S. Pohokar has done a research on optimization of process parameters of gas metal arc welding to improve the quality of weld bead geometry. In their work, a grey-based Taguchi method was adopted to optimize the gas metal arc welding process parameters. Many quality characteristic parameters were combined into one integrated quality parameter by using grey relational grade or rank. The welding parameters considered in their research were arc voltage, wire feed rate, welding speed, nozzle to plate distance and gas flow. The quality characteristics consider were penetration, reinforcement and bead width. Analysis of variance has performed to find the effect of individual process parameter on quality parameters. The Taguchi L25 array was adopted to conduct the experiments. The stainless steel (AISI410) was used as welding specimen. [2]
A.K. Panday, M.I. khan & K.M. Moeed performed their analysis on optimization of resistance spot welding parameters using Taguchi method. The experiments were conducted under varying pressure, welding current and welding time. The output characteristic considered was
tensile strength of the welded joint. The material used was low carbon steel sheets of 0.9mm. Their conclusion leads that the contribution of welding current holding time and pressure towards tensile strength is 61%, 28.7% and 4 % respectively as determined by the ANOVA method. [3]
Nirmalendu Choudhary, Asish Bandypadhay & Ramesh Rudrapati has presented their work on design optimization of process parameters of TIG welding using Taguchi method. They considered welding current, gas flow rate and filler rod as input process parameters and optimizes their values using Taguchi method to improve the ultimate load on weld materials. Taguchi design of experiment, S/N ratio and ANOVA analysis has performed for optimizing the results. The stainless steel slabs & mild steel slabs were used as work piece material. The optimum welding condition is obtained by the Taguchi method: current= 100A, gas flow rate= 18 l/min, filler rod diameter = 2mm. Confirmation test confirmed the optimum values. [4]
-
Patil & C.A Waghmare presented their work on optimization of MIG welding parameters for improving welding strength. They presents the influence of welding parameters welding current, welding voltage, welding speed on ultimate strength of welded joints of AISI mild steel materials. A plan of experiments using Taguchi has decided. Experiments were performed and result was confirmed. From this study they concluded that the welding current and welding speed are the major factors affecting tensile strength of welded joints. [5]
Ajit hooda, Ashwani dhingra & Satpal sharma has done their research on optimization of MIG welding parameters in order to improve yield strength of AISI 1040 mild steel. The process parameters welding current, voltage, gas flow rate and wire speed were studied. The experiments were conducted based on four factors, three level orthogonal arrays. The empirical relationship can be used to predict the yield strength of welded material.
[6]In the present work, it is planned to analyse the different input parameters in MIG welding to improve the ultimate tensile strength and Hardness of the welding joint using Taguchis orthogonal array.
-
-
EXPERIMENTAL DETAILS
The experiments have been conducted using a Pulsed Current Lorch welding machine having 400 Amperes maximum current with air type cooling and automated welding set up. In this welding machine automated Metal Inert Gas torches as well as automatic feeler wire feeding units have provided.
-
Material selection
The present study has been carried out with ASTM A106 pipes is the standard specification for seamless carbon steel pipes for high-temperature service. Most common uses are in refineries and plants when gasses or fluids are transported at high temperatures and pressures.
TABLE I. CHEMICAL COMPOSITIONS OF ASTM A106
-
Taguchi Orhogonal Array Selection
The Taguchi method developed by Genuchi Taguchi is a statistical method used to improve the product quality. Optimization of process parameters is the key step in the Taguchi method for achieving high quality without increasing cost. This is because optimization of process parameters can improve quality characteristics and the optimal process parameters obtained from the Taguchi method are insensitive to the variation of environmental conditions and other noise factors. Basically, classical process parameter design is complex and not easy to use. A large number of experiments have to be carried out when the number of process parameters increases. To solve this task, the Taguchi method uses a special design of orthogonal arrays to study the entire process parameter space with a small number of experiments only. A loss function is then defined to calculate the deviation between the experimental value and the desired value. Taguchi recommends the use of the loss function to measure the deviation of the quality characteristic from the desired value. The value of the loss function is further transformed into signal-to-noise (S/N) ratio.
-
S/N Ratio
The signal to noise ratios (S/N), which are log functions of desired output, serve as the objective functions for optimization, help in data analysis and the prediction of the optimum results. There are 2 Signal-to-Noise ratios of common interest for optimization of Static Problems.
2
2
-
Smaller the better is given by = -10 log [(Yi /n]
2
2
-
Larger the better is given by = -10 log [( 1/Yi )/ n]
Where, = Signal to Noise ratio Yi= ith observed value of response n = no. of observations in a trial
y = average of observed response.
-
-
Experimental parameter
Input parameters: Welding Current, Gas Flow Rate and Wire Feed Rate.
Output parameters: Ultimate Tensile Strength (UTS) and Percentage of Elongation (PE).
TABLE II. CONTROL FACTORS AND THEIR LEVEL
Sl.No
Symbol
Factors
Level 1
Level 2
Level 3
1
A
Welding Current (Ampere)
55
60
65
2
B
Gas Flow Rate (l/min)
12
13
14
3
C
Wire Feed Rate (mm/min)
110
115
120
-
Proposed Design of Experiment
For performing the experiments Taguchi L27 orthogonal array was selected for 3-factor and 3-level process parameters and which reduces the number of experiments which is given in table 3.
Element
C
Mn
P
S
Si
Cr
Ni
%
0.30
0.29
0.035
0.035
0.10
0.40
0.40
TABLE III. TAGUCHI L27 ORTHOGONAL ARRAY
No. of Runs
A
B
C
1
1
1
1
2
1
1
2
3
1
1
3
4
1
2
1
5
1
2
2
6
1
2
3
7
1
3
1
8
1
3
2
9
1
3
3
10
2
1
1
11
2
1
2
12
2
1
3
13
2
2
1
14
2
2
2
15
2
2
3
16
2
3
1
17
2
3
2
18
2
3
3
19
3
1
1
20
3
1
2
21
3
1
3
22
3
2
1
23
3
2
2
24
3
2
3
25
3
3
1
26
3
3
2
27
3
3
3
-
Experimental Work
Experiments were conducted using Pulsed current Lorch welding machine by DC electrode positive power supply. Test pieces of size outer diameter of 25 mm, length of 300 mm with wall thickness of 3mm were cut in to length of each 150 mm initially with an edge preparation of 45 degree and tack welded as shown in Fig. 2. Copper coated Mild steel electrode of 1.2 mm diameter was used for welding. Argon (85%) and CO2 (15%) gas mixture was used for shielding. The experimental setup used consists of a rotating disk in to which work sample was attached as shown in Fig. 3. Welding speed (157 mm/min) has been kept constant for all twenty trails. Single pass welding was performed on pipes by varying the initial parameters as shown in Table 3. The working ranges for the process parameters were selected from the American Welding Society handbook. Based on the designed L27 orthogonal array combination a series of joining processes was performed in welding machine. Ultimate Tensile Strength and Percentage of Elongation are considered as objectives. For the calculation of the responses of welded specimens, tensile test were performed using Advanced Universal Testing Machine model number; UTM US-1000 kN and make; Akash Industries.
Fig. 2. Sample specimen with dimension
Fig. 3. Experimental Setup
TABLE IV. EXPERIMENTAL DESIGN MATRIX AND RESULTS
No. of Runs
Current (Amp)
Gas Flow Rate (l/min)
Wire Feed Rate (mm/min)
UTS
(N/mm2)
PE (%)
1
55
12
110
303
5.1
2
55
12
115
235
3.1
3
55
12
120
207
1.9
4
55
13
110
215
2.6
5
55
13
115
204
2.3
6
55
13
120
225
3.1
7
55
14
110
195
1.9
8
55
14
115
232
2.8
9
55
<>14 120
172
1.6
10
60
12
110
258
4.1
11
60
12
115
229
2.5
12
60
12
120
245
4.5
13
60
13
110
210
2.2
14
60
13
115
256
4.1
15
60
13
120
189
2
16
60
14
110
250
5.5
17
60
14
115
202
2.7
18
60
14
120
162
1.5
19
65
12
110
306
6.1
20
65
12
115
375
6.5
21
65
12
120
255
4
22
65
13
110
335
6.9
23
65
13
115
276
3.5
24
65
13
120
209
3.5
25
65
14
110
245
5.1
26
65
14
115
251
3.6
27
65
14
120
256
5
-
ANOVA table and response calculation
The purpose of the Analysis of Variance (ANOVA) is to examine which design parameters significantly affect the quality characteristic. This is accomplished by separating the total variability of the S/N ratios, which is measured by the sum of the squared deviations from the total mean S/N ratio, into contributions by each of the parameters and the error. The ANOVA table for both UTS and PE are shown in table 5 and
6. The response table for both UTS and PE are shown in table 7 and 8
TABLE V. ANOVA TABLE FOR UTS
Source
DOF
SS
MS
F
P (%)
A
2
22.863
11.432
10.67
31.75***
B
2
14.226
7.113
6.64
19.76**
C
2
13.472
6.376
6.29
18.71*
Error
2
21.428
1.071
Total
8
71.989
SS. sum of squares; DOF. degree of freedom;
P. percentage contribution;
*. level of significance at 95% confidence level
TABLE VI. ANOVA TABLE FOR PE
Source
DOF
SS
MS
F
P (%)
A
2
140.71
70.370
44.34
56.6***
B
2
30.375
15.187
9.57
12.23**
C
2
48.640
24.320
15.32
19.58*
Error
2
28.566
Total
8
248.291
TABLE VII. RESPONSE TABLE FOR UTS
Levels
A
B
C
1
46.66
48.05
47.85
2
46.85
47.31
47.70
3
48.58
46.73
46.54
Delta
1.93
1.32
1.31
Rank
1
2
3
TABLE VIII. RESPONSE TABLE FOR PE
Levels
A
B
C
1
8.168
11.871
12.064
2
9.484
9.892
10.346
3
13.534
9.424
8.777
Delta
5.365
2.447
3.287
Rank
1
3
2
The ANOVAs table and response table calculation represents the effect of different parameters on individual responses.
Here *** & ** represents most significant and significant parameters and * as less significant for individual response.
-
-
RESULT AND DISCUSSION
Mean of SN ratios
Mean of SN ratios
-
Optimum parameter selection from S/N ratio for UTS Ultimate Tensile Strength is larger-the-better type quality characteristic. Therefore higher values of Ultimate Tensile Strength (UTS) are considered to be optimal. It is clear from Fig. 4, that Ultimate Tensile Strength is highest at third level of welding current, first level of gas flow rate and first level of wire feed rate (A3B1C1).
Main Effects Plot for SN ratios
Data Means
A B
Main Effects Plot for SN ratios
Data Means
A B
48.5
48.0
47.5
47.0
46.5
55
60
C
65
12
13
14
48.5
48.0
47.5
47.0
46.5
55
60
C
65
12
13
14
110
115
120
110
115
120
Signal-to-noise: Larger is better
Signal-to-noise: Larger is better
48.5
48.0
47.5
47.0
46.5
48.5
48.0
47.5
47.0
46.5
Fig. 4. Main Effects Plot for UTS
-
Optimum parameter selection from S/N ratio for percentage of elongation
Mean of SN ratios
Mean of SN ratios
Percentage of elongation is larger-the-better type quality characteristic. Therefore higher values of Percentage of elongation are considered to be optimal. It is clear from Fig. 5, that Percentage of elongation of is highest at third level of welding current, first level of gas flow rate and first level of wire feed rate (A3B1C1).
Main Effects Plot for SN ratios
Data Means
Current Gas Flow Rate
14
12
10
8
Main Effects Plot for SN ratios
Data Means
Current Gas Flow Rate
14
12
10
8
110
115
120
110
115
120
Signal-to-noise: Larger is better
Signal-to-noise: Larger is better
55
55
60
Wire Feed rate
60
Wire Feed rate
65
65
12
12
13
13
14
14
14
12
10
8
14
12
10
8
Fig. 5. Main Effects plots for Percentage of Elongation
-
Analysis of Variance (ANOVA) Results for UTS
The calculated values of Analysis of Variance for Ultimate Tensile Strength of welding joint are listed in table 5. The calculated values of ANOVA present the percentage effect of
each parameter on Ultimate Tensile Strength of the joint. From the analysis, it is seen that current is the most contribution factor and the wire feed rate is the least contribution factor for UTS of joint.
-
Analysis of Variance (ANOVA) Results for PE
The calculated values of Analysis of variance for percentage elongation of welding joint are listed in table 6. The calculated values of ANOVA present the percentage effect of each parameter on hardness of the joint. From the analysis, it is seen that current is the most contribution factor and the gas flow rate is the least contribution factor for Percentage of elongation.
-
-
VERIFICATION EXPERIMENT
The confirmation run was conducted using same experimental setup by taking optimized parameters for the ASTM A106 pipes considered in this present work. The results obtained from the confirmation runs are tabulated in the below Table 9.
TABLE IX. RESULTS OF VERIFICATION EXPERIMENT
Initial Parameter Readings
Optimized Parameter Readings
Parameter
UTS
(N/mm2)
PE (%)
UTS
(N/mm2)
PE (%)
Current (Ampere)
65
65
65
65
Gas Flow Rate (l/min)
12
12
12
12
Wire Feed Rate (mm/min)
115
115
110
110
Response Obtained
375
6.5
383
8.5
Form the table 9; one can observe that, the optimized parameters have considerable effect on the response variables
i.e. Ultimate Tensile Strength (UTS) and percentage elongation of ASTM A106 pipes. Ultimate Tensile Strength (UTS) was at 375 N/mm2 for initial settings of parameters and the value has been increased to 383 N/mm2 after setting parameters to optimized values. Similarly, the PE has been increased from 6.5 % to 8.5 %
-
CONCLUSION
In this present work the optimization of the process parameters for Pulsed Gas Metal Arc welding of ASTM A106 pipes with larger Ultimate Tensile Strength and percentage elongation has been reported. A Taguchi orthogonal array, the signal-to-noise (S/N) ratio and Analysis of Variance (ANOVA) were used for the optimization of welding parameters and it is found that i) optimum condition for maximum UTS is (A3B1C1) i.e. current = 65 Ampere,
gas flow rate = 12 liter/min and wire feed rate = 110 mm/min
ii) optimum condition for maximum percentage elongation is (A3B1C1) i.e. current = 65 Ampere gas flow rate = 12 liter/min and wire feed rate = 110 mm/min. ANOVA for UTS shows that current is the most significant factor, followed by gas flow rate. ANOVA for percentage elongation indicates that current influences most significantly, followed by wire feed rate. Conformation experiment was also conducted and verified the effectiveness of the Taguchi optimization method.
ACKNOWLEDGMENT
We express our sincere thanks to Mr. Bhasheer Ahmed, Director, Sri Sidhi Vinaya fabrications Pvt. Ltd. Banglore, INDIA and P.E.S College of Engineering, Mandya, INDIA for their support to carry out experimentation.
REFERENCES
-
Lin C.L. and Lin. J.L, (2002), The use of orthogonal array with Grey relational analysis to optimize the EDM process with multiple performance characteristics. J Matar Process tacnol, pp. 237-244.
-
S.V Sapakal, M.T. Telsang,(2012),Parametric optimization of MIG welding using Taguchi design method. International Journal Of advanced Engineering Research and Studies, pp. 28-30.
-
S.R. Meshram, N.S. pohokar, (2013), Optimization of process parameters of Gas Metal Arc Welding to improve Quality of Weld bead geometry. International journal of Engineering, Business and Enterprise Applications, pp. 46-52.
-
A.K. Panday, M.I. Khan, K.M. Moeed, (2013), Optimization of resistance welding parameters using Taguchi method. International Journal of Engineering Science and Technology, pp. 243-240.
-
Nirmalendhu Choudhury, Asish Bandyopadhyay, Ramesh Rudrapati, (2014), Design optimization of process parameters for TIG welding based on Taguchi method. International Journal of Current Engineering and Technology, pp. 12-16.
-
S.R. Patil, C.A. Waghmare, (2013), Optimization of MIG welding parameters for improving strength of welded joints. International Journal of Advanced Engineering and Studies, pp.14-16.
-
Ajit Hooda, Ashwani Dhingra and Satpal Sharma, (2012) , Optimization of Welding process parameters to predict maximum yield strength in AISI 1040. International Journal of mechanical Engineering and Robotics Research India, pp. 204-212.