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Experimentation of effect of process parameters on mechanical properties in SAW Process
Available online at www.sciencedirect.com
ScienceDirect Materials Today: Proceedings 5 (2018) 26961–26967
www.materialstoday.com/proceedings
ICAMM_2016
Experimentation of effect of process parameters on mechanical properties in SAW Process B.Venugopal raoa*, N.Aravindana, K. Saraswathammaa 1
Department of Mechanical Engineering, University College of Engineering, Osmania University, Hyderabad, India
Abstract Submerged Arc Welding (SAW) is most preferred metal joining process for high thickness weld material due to its productivity , ease of automation and less welding skill requirement. Weld quality and cost effectiveness of welding can be determined by analyzing mechanical properties of weld and influence of process parameters. The quality of weld bead is mainly affected by process parameters like current, voltage, weld speed, polarity, wire feed rate, thickness of material, nozzle gap etc., In this work, attempt has been made to find the effect of process parameters such as welding current, voltage and welding speed of automatic SAW on carbon steel SA 516 grade 70 material using EA-3 electrode material on the mechanical properties such as tensile strength, hardness of the weld zone and heat affected zone(HAZ) and microstructure. Experiments have been conducted as per Taguchi method using L9 array. ANOVA (Analysis of Variance) was done to find out most influencing parameter that effect mechanical properties of the weld quality. The results revealed that in weld bead, voltage is the most significant factor for tensile strength and current is the most significant factor for hardness of weld zone and HAZ. © 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of ICAMM-2016. Keywords: Submerged Arc Welding ;Taguchi ; ANOVA ;Carbon steel SA 516 grade 70;
1. Introduction In the manufacturing sector, Micro fabrication finds good attention among researchers. In recent years, number of research works carried out in micro fabrication due to the rapid need of micro components to cater the demand of house hold industrial application. Micro channels can be defined as channels whose dimensions are less than 1 millimeter and greater than 1 micron. The advantages are mainly due to their high surface-to-volume ratio and small volumes. The large surface-to-volume ratio leads to high rate of heat and mass transfer. * Corresponding author. Tel.: +91-94909 59974. E-mail address: [email protected] 2214-7853 © 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of ICAMM-2016.
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B.Venugopal Rao/ Materials Today: Proceedings 5 (2018) 26961–26967
Micro channels of various shapes and depths are key elements in various surface textures. Micro channels generally with non-traditional machining like Micro EDM, Wire EDM, Laser machining, Wire Electric discharge machining, photolithography etc. Micro-EDM was used to fabricate the channels by employing very fine wires as electrodes. Ultrasonic and water jet cutting are also successfully employed particularly for very hard materials. Electroforming, molding, and stereolithographic fabrication have been used successfully into the micro regime through the incorporation of lithographic and laser-based patterning. 2. Literature review Edwin & Kumanan [1] made an attempt to minimise the weld bead width using optimisation procedures based on the Genetic algorithm (GA) and Paricle swarm optimisation (PSO) algorithm to determine optimal weld parameters. They conducted experiments as per Taguchi L8 orthogonal array, in semi automatic SAW machine. They found both GA and PSO can be used in optimising the parameters of a SAW machine and developed PSO algorithm as a power tool in experimental welding optimisation. Prasanta, Tapan & Sujit [2] developed a prediction model for steel weld metal mechanical properties as a function of flux ingredients such as CaO, MgO, CaF2 and Al2O3 in Submerged Arc welding carried out at fixed welding parameters. They found that flux ingredients i) CaO improves the mechanical properties like Yield strength, Ultimate Tensile strength, Charpy impact toughness, ii) MgO improves the mechanical properties iii) interaction MgO with CaF2 and Al2O3 is detrimental to mechanical properties. Ankush, Agarwal & Amarjeet [3] analysed welding input parameters play significant role in determing the quality of welds. Bead geometry, mechanical properties and distortion by comparing input parameters. The study of effect of polarity change affects the amount of heat generated at welding electrode and work piece. Hence influences the metal deposition rate, weld bead, HAZ and mechanical properties of the weld metal. Pratik & Sandip [4] analysed optimization of SAW process in SS 304 material by using process parameters like welding current, arc voltage and welding speed and the output parameters are hardness, tensile strength and microstructure of material. It was found that plate thickness is found most significant effect on penetration and bead width. Amrat & Ashish [5] analysed comprehensive research review on effect of Arc welding parameter on quality of welds. They found that effect of polarity change affects the amount of heat generated at welding electrode and work piece. Hence influences metal deposition rate, weldbead, HAZ and mechanical properties of the weld metal. Tomasz [6] studied the modified submerged welding process, where additional cold wire is inserted in to the arc. The influence of welding parameters on process efficiency has been determined and a preliminary procedure for welding butt joint of DH36 ship building steel. The modified method with additional cold electrode wire fulfilled the requirements of marine classification societies. 3. Problem Definition From the Literature review, it is understood that number of research works done on theoretical analysis on the materials like of C-Mn steel , 1.25 Cr-0.5 steel, X 100 pipe line steel, ASTM A36 mild steel plate , DH36 ship building steel etc. It is also understood from the review there were less attempt made to optimize the process parameters SAW on SA516 grade 70 carbon steel, which was used in various applications like LPHT (low pressure heat exchanger), oil cooler, gas cooler, condenser, ejector etc. Further SA516 grade 70 carbon steel is exclusively used in Shell manufacturing to cater the need of various power plant applications using SAW process. Hence, in this work an attempt is made to optimize the process parameters SAW on SA516 grade 70 carbon steel mechanical properties and Microstructure.
B.Venugopal Rao/ Materials Today: Proceedings 5 (2018) 26961–26967
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4. Research methodology 4.1 Taguchi method Taguchi method employs a specially designed orthogonal array to study the entire factor space through only a small number of experiments. In this method, the term “signal” represents the desirable value for the output characteristic and the term “noise’ represents the undesirable value for the output characteristic. Therefore, the term ‘signal-to-noise ratio” or S/N ratio in Taguchi method measures the deviation of quality characteristic from the desired value. 4.2 First stage In the First stage of Taguchi methodology, process parameters and their levels were identified. From the Literature review, welding current, arc voltage and welding speed were found to be most important factors which affect the weld bead quality in SAW process. The process parameters range is selected as per the expert guidance and literature review and tabulated in Table 1. Table 1 : Process parameters and their levels Sl. No. 1 2. 3.
Parameters
Code
Welding Current (A) Arc Voltage (V) Welding speed (mm/min)
A B C
1 475 26 300
Level 2 525 30 400
3 600 34 500
4.3 Second stage According to the Orthogonal array for 3- level, 3 factors L9 array selected and process control parameters and their levels are arranged in experimental matrix and tabulated in Table 2. Table 2 Experimental matrix adopted as per L9 Orthogonal array Experiment No. 1 2 3 4 5 6 7 8 9
Current (A) 475 475 475 525 525 525 600 600 600
Parameters Voltage Welding speed (V) (mm/min) 26 300 30 400 34 500 26 400 30 500 34 300 26 500 30 300 34 400
4.4 Third stage The experiments were conducted in fabrication section of Pulverisor unit of M/s BHEL, Ramachandrapuram on ESAB column and boom machine. Carbon steel SA516 grade 70 used as the base metal and AWS-EA 3 having diameter of 4 mm used as the electrode wire. Flux used for the study was Tapadia Gr 80. The corresponding values are tabulated in Table 3.
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B.Venugopal Rao/ Materials Today: Proceedings 5 (2018) 26961–26967 Table 3 Output responses of each experiment Experiment No.
Current (A)
Parameters Voltage (V)
1 2 3 4 5 6 7 8 9
475 475 475 525 525 525 600 600 600
26 30 34 26 30 34 26 30 34
Welding speed (mm/min) 300 400 500 400 500 300 500 300 400
Tensile strength (N/mm2)
Responses Weld hardness (BHN)
HAZ hardness (BHN)
191 180 187 180 187 184 202 207 211
187 187 180 187 184 187 211 195 202
612.41 657.89 619.02 539.43 612.41 657.89 541.83 625.96 650.94
5. Analysis of Results According to Taguchi method, S/N ratio is the ratio of “Signal” representing desirable value, i.e., mean of output characteristics and the “noise” representing the undesirable value, i.e., squared deviation of the output characteristics. S/N ratio is used to measure quality characteristic and it is also used to measure significant parameters. S/N ratio Eqn (1) has three forms the characteristics which are classified as Higher the best (HB), Lower the best (LB) and Nominal the Best (NB). These are of common interest for optimization of static problems.
=
-10 𝑙𝑜𝑔10 (MSD)
Where ,
(1)
S/N ratio
For Higher the best Mean Square Deviation (MSD) = For Lower the best Mean Square Deviation (MSD) =
( (
)
(2)
)
(3)
y – Value of Quality Characteristics n - no. of trials Higher value of tensile strength is most desired. Hence the quality characteristic for this response is “Higher the best” Eqn (1) choosen. Lower value of weld hardness & HAZ hardness is most desired. So the quality characteristic for those responses are “ Lower the best” choosen Eqn (2) . Based on the above formulae S/N ratios were calculated and tabulated in Table 4. Table 4. S/N ratios of the responses Factors Experim ent No. 1 2 3 4 5 6 7 8 9
A Current (A) 475 475 475 525 525 525 600 600 600
B Voltage (V) 26 30 34 26 30 34 26 30 34
S/N ratio C Welding speed (mm/min) 300 400 500 400 500 300 500 300 400
Tensile strength 39.79 27.52 34.54 24.48 26.54 28.01 29.12 23.02 29.09
weld Hardness
HAZ hardness
-45.62 -45.11 -45.44 -45.11 -45.44 -45.30 -46.11 -46.32 -46.49
-45.44 -45.44 -45.11 -45.44 -45.30 -45.44 -46.49 -45.80 -46.11
B.Venugopal Rao/ Materials Today: Proceedings 5 (2018) 26961–26967
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5.1 Effect of process parameters on Tensile strength Regardless of the Quality characteristics, the higher S/N ratio gives the optimum condition. Accordingly from the Figure 1, it is observed that Current at Level I, voltage at Level III and Welding speed at Level I are having higher S/N ratio. As such the optimum parameter for turning is A1B3C1. The Corresponding values are Current 475 A, voltage 34 v and welding speed is 300 mm/min.
Fig. 1. S/N Ratios Vs Factors Table 5 ANOVA table for Tensile strength Factors current voltage welding speed Error Total
DOF
Sum of squares
Variance
F
2 2 2 2 8
0.29 2.30 0.53 0.33 3.44
0.14 1.15 0.27
0.87 7.04 1.63
Percentage contribution 8.29 66.75 15.48 9.48 100.00
From the ANOVA Table 5, it is observed that the most significant factor affecting Tensile strength is Voltage with a contribution of 66.75%. 5.2 Effect of process parameters on Weld Hardness Regardless of the Quality characteristics, the higher S/N ratio gives the optimum condition. Accordingly from the above graphs shown in fig.2 it is observed that Current at Level II, voltage at Level I and Welding speed at Level II are having higher S/N ratio. As such the optimum parameter for turning is A2B1C2. The Corresponding values are Current 525 A, voltage 26 v and welding speed is 400 mm/min
Fig. 2 . S/N Ratios Vs Factors
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B.Venugopal Rao/ Materials Today: Proceedings 5 (2018) 26961–26967
Table 6. ANOVA table for Weld Hardness
Factors
DOF
Sum of squares
2 2 2 2 8
1.90 0.03 0.05 0.18 2.16
Current Voltage Welding speed Error Total
Percentage contribution
Variance
F
0.95 0.02 0.02
10.31 0.17 0.26
87.82 1.42 2.25 8.51 100.00
From the ANOVA Table No. 6 , it is observed that the most significant factor affecting Weld hardness is Current with a contribution of 87.82 %. 5.3 Effect of process parameters on Heat Affected Zone Hardness Regardless of the Quality characteristics, the higher S/N ratio gives the optimum condition. Accordingly from the graphs shown in fig. 3 it is observed that Current at Level I, voltage at Level II and Welding speed at Level I are having higher S/N ratio. As such the optimum parameter for turning is A1B2C1. The Corresponding values are Current 475 A, voltage 30 v and welding speed is 300 mm/min
Fig. 3. S/N Ratios Vs Factors Table 7. ANOVA table for HAZ hardness Factors Current Voltage Welding speed Error Total
DOF 2 2 2 2 8
Sum of squares 1.20 0.13 0.02 0.17 1.52
Variance
F
0.60 0.07 0.01
6.97 0.77 0.10
Percentage contribution 78.87 8.75 1.08 11.31 100.00
From the above ANOVA Table 7, it is observed that the most significant factor affecting HAZ hardness is Current with a contribution of 78.87 %.
B.Venugopal Rao/ Materials Today: Proceedings 5 (2018) 26961–26967
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5.4 Microstructure analysis of Weld bead
Fig. 4 Microstructure of weld bead
The microstructures are studied in different zones like base metal, heat affected zone and weld zones. From the Figure 4 , the microstructure of the center of weld zone is found to be different from the heat-affected zone. The base microstructure consists of pearlite and ferrite banding in the matrix. The heat affected zone (HAZ) consists of coarse grain boundary ferrite, Widmanstatten ferrite and acicular ferrite in the matrix. The weld zone consists of network of columnar grains of grain boundary ferrite, acicular ferrite and Widmanstatten ferrite in the matrix. The hardness values of weld zone and heat affected zone (HAZ) didn’t follow any specific pattern, however the values are found to be in the similar range. 6.Conclusions From the analysis it is inferred that the optimal process parameters for Weld bead Tensile strength are Current (475A), Voltage (34 V) and Welding speed (300 mm/min), Optimal parameters for Weld hardness are Current (525A), Voltage (26 V) and Welding speed (400 mm/min) and Optimal parameters for HAZ hardness are Current (475A), Voltage (30 V) and Welding speed (300 mm/min). Voltage is found to be most influencing factor in deciding Tensile strength and Current is found to be most influencing factor in deciding Weld hardness and Heat affected zone hardness. References
[1] [2] [3] [4] [5] [6]
Edwin, Kumanan, optimization of submerged arc weld using non conventional techniques, Applied soft computing 11 (2011)5198-5204. Prasanta,Tapan, Sujit, Prediction of mechanical properties in submerged arc weld metal of C-Mn steel, Material and Manufacturing processes, 22: 114-127,2007 Ankush Batta, J K Agarwal Varinder Khurana, Amarjeet singh sandhu ,Optimisation of Submerged Arc Welding Process: A Review, IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE), Volume 12, 39-44,2015. Pratik Umrigar, Prof. Sandip J. Chaudhry, Parametric Optimization of Submerged Arc Welding On Stainless Steel- 304, International Journal for Technological Research in Engineering, Volume I, 2347-4718,2014. Amrat M. Patel Dr, Ashish V Gohil , International Journal of pure and applied research in Engineering and Technology, Review Article, Volume 1 (6),2013.. Tomasz Kozak, Submerged arc welded with an increased efficiency, Welding International, Vol 29, 614-618, 2015.
ScienceDirect Materials Today: Proceedings 5 (2018) 26961–26967
www.materialstoday.com/proceedings
ICAMM_2016
Experimentation of effect of process parameters on mechanical properties in SAW Process B.Venugopal raoa*, N.Aravindana, K. Saraswathammaa 1
Department of Mechanical Engineering, University College of Engineering, Osmania University, Hyderabad, India
Abstract Submerged Arc Welding (SAW) is most preferred metal joining process for high thickness weld material due to its productivity , ease of automation and less welding skill requirement. Weld quality and cost effectiveness of welding can be determined by analyzing mechanical properties of weld and influence of process parameters. The quality of weld bead is mainly affected by process parameters like current, voltage, weld speed, polarity, wire feed rate, thickness of material, nozzle gap etc., In this work, attempt has been made to find the effect of process parameters such as welding current, voltage and welding speed of automatic SAW on carbon steel SA 516 grade 70 material using EA-3 electrode material on the mechanical properties such as tensile strength, hardness of the weld zone and heat affected zone(HAZ) and microstructure. Experiments have been conducted as per Taguchi method using L9 array. ANOVA (Analysis of Variance) was done to find out most influencing parameter that effect mechanical properties of the weld quality. The results revealed that in weld bead, voltage is the most significant factor for tensile strength and current is the most significant factor for hardness of weld zone and HAZ. © 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of ICAMM-2016. Keywords: Submerged Arc Welding ;Taguchi ; ANOVA ;Carbon steel SA 516 grade 70;
1. Introduction In the manufacturing sector, Micro fabrication finds good attention among researchers. In recent years, number of research works carried out in micro fabrication due to the rapid need of micro components to cater the demand of house hold industrial application. Micro channels can be defined as channels whose dimensions are less than 1 millimeter and greater than 1 micron. The advantages are mainly due to their high surface-to-volume ratio and small volumes. The large surface-to-volume ratio leads to high rate of heat and mass transfer. * Corresponding author. Tel.: +91-94909 59974. E-mail address: [email protected] 2214-7853 © 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of ICAMM-2016.
26962
B.Venugopal Rao/ Materials Today: Proceedings 5 (2018) 26961–26967
Micro channels of various shapes and depths are key elements in various surface textures. Micro channels generally with non-traditional machining like Micro EDM, Wire EDM, Laser machining, Wire Electric discharge machining, photolithography etc. Micro-EDM was used to fabricate the channels by employing very fine wires as electrodes. Ultrasonic and water jet cutting are also successfully employed particularly for very hard materials. Electroforming, molding, and stereolithographic fabrication have been used successfully into the micro regime through the incorporation of lithographic and laser-based patterning. 2. Literature review Edwin & Kumanan [1] made an attempt to minimise the weld bead width using optimisation procedures based on the Genetic algorithm (GA) and Paricle swarm optimisation (PSO) algorithm to determine optimal weld parameters. They conducted experiments as per Taguchi L8 orthogonal array, in semi automatic SAW machine. They found both GA and PSO can be used in optimising the parameters of a SAW machine and developed PSO algorithm as a power tool in experimental welding optimisation. Prasanta, Tapan & Sujit [2] developed a prediction model for steel weld metal mechanical properties as a function of flux ingredients such as CaO, MgO, CaF2 and Al2O3 in Submerged Arc welding carried out at fixed welding parameters. They found that flux ingredients i) CaO improves the mechanical properties like Yield strength, Ultimate Tensile strength, Charpy impact toughness, ii) MgO improves the mechanical properties iii) interaction MgO with CaF2 and Al2O3 is detrimental to mechanical properties. Ankush, Agarwal & Amarjeet [3] analysed welding input parameters play significant role in determing the quality of welds. Bead geometry, mechanical properties and distortion by comparing input parameters. The study of effect of polarity change affects the amount of heat generated at welding electrode and work piece. Hence influences the metal deposition rate, weld bead, HAZ and mechanical properties of the weld metal. Pratik & Sandip [4] analysed optimization of SAW process in SS 304 material by using process parameters like welding current, arc voltage and welding speed and the output parameters are hardness, tensile strength and microstructure of material. It was found that plate thickness is found most significant effect on penetration and bead width. Amrat & Ashish [5] analysed comprehensive research review on effect of Arc welding parameter on quality of welds. They found that effect of polarity change affects the amount of heat generated at welding electrode and work piece. Hence influences metal deposition rate, weldbead, HAZ and mechanical properties of the weld metal. Tomasz [6] studied the modified submerged welding process, where additional cold wire is inserted in to the arc. The influence of welding parameters on process efficiency has been determined and a preliminary procedure for welding butt joint of DH36 ship building steel. The modified method with additional cold electrode wire fulfilled the requirements of marine classification societies. 3. Problem Definition From the Literature review, it is understood that number of research works done on theoretical analysis on the materials like of C-Mn steel , 1.25 Cr-0.5 steel, X 100 pipe line steel, ASTM A36 mild steel plate , DH36 ship building steel etc. It is also understood from the review there were less attempt made to optimize the process parameters SAW on SA516 grade 70 carbon steel, which was used in various applications like LPHT (low pressure heat exchanger), oil cooler, gas cooler, condenser, ejector etc. Further SA516 grade 70 carbon steel is exclusively used in Shell manufacturing to cater the need of various power plant applications using SAW process. Hence, in this work an attempt is made to optimize the process parameters SAW on SA516 grade 70 carbon steel mechanical properties and Microstructure.
B.Venugopal Rao/ Materials Today: Proceedings 5 (2018) 26961–26967
26963
4. Research methodology 4.1 Taguchi method Taguchi method employs a specially designed orthogonal array to study the entire factor space through only a small number of experiments. In this method, the term “signal” represents the desirable value for the output characteristic and the term “noise’ represents the undesirable value for the output characteristic. Therefore, the term ‘signal-to-noise ratio” or S/N ratio in Taguchi method measures the deviation of quality characteristic from the desired value. 4.2 First stage In the First stage of Taguchi methodology, process parameters and their levels were identified. From the Literature review, welding current, arc voltage and welding speed were found to be most important factors which affect the weld bead quality in SAW process. The process parameters range is selected as per the expert guidance and literature review and tabulated in Table 1. Table 1 : Process parameters and their levels Sl. No. 1 2. 3.
Parameters
Code
Welding Current (A) Arc Voltage (V) Welding speed (mm/min)
A B C
1 475 26 300
Level 2 525 30 400
3 600 34 500
4.3 Second stage According to the Orthogonal array for 3- level, 3 factors L9 array selected and process control parameters and their levels are arranged in experimental matrix and tabulated in Table 2. Table 2 Experimental matrix adopted as per L9 Orthogonal array Experiment No. 1 2 3 4 5 6 7 8 9
Current (A) 475 475 475 525 525 525 600 600 600
Parameters Voltage Welding speed (V) (mm/min) 26 300 30 400 34 500 26 400 30 500 34 300 26 500 30 300 34 400
4.4 Third stage The experiments were conducted in fabrication section of Pulverisor unit of M/s BHEL, Ramachandrapuram on ESAB column and boom machine. Carbon steel SA516 grade 70 used as the base metal and AWS-EA 3 having diameter of 4 mm used as the electrode wire. Flux used for the study was Tapadia Gr 80. The corresponding values are tabulated in Table 3.
26964
B.Venugopal Rao/ Materials Today: Proceedings 5 (2018) 26961–26967 Table 3 Output responses of each experiment Experiment No.
Current (A)
Parameters Voltage (V)
1 2 3 4 5 6 7 8 9
475 475 475 525 525 525 600 600 600
26 30 34 26 30 34 26 30 34
Welding speed (mm/min) 300 400 500 400 500 300 500 300 400
Tensile strength (N/mm2)
Responses Weld hardness (BHN)
HAZ hardness (BHN)
191 180 187 180 187 184 202 207 211
187 187 180 187 184 187 211 195 202
612.41 657.89 619.02 539.43 612.41 657.89 541.83 625.96 650.94
5. Analysis of Results According to Taguchi method, S/N ratio is the ratio of “Signal” representing desirable value, i.e., mean of output characteristics and the “noise” representing the undesirable value, i.e., squared deviation of the output characteristics. S/N ratio is used to measure quality characteristic and it is also used to measure significant parameters. S/N ratio Eqn (1) has three forms the characteristics which are classified as Higher the best (HB), Lower the best (LB) and Nominal the Best (NB). These are of common interest for optimization of static problems.
=
-10 𝑙𝑜𝑔10 (MSD)
Where ,
(1)
S/N ratio
For Higher the best Mean Square Deviation (MSD) = For Lower the best Mean Square Deviation (MSD) =
( (
)
(2)
)
(3)
y – Value of Quality Characteristics n - no. of trials Higher value of tensile strength is most desired. Hence the quality characteristic for this response is “Higher the best” Eqn (1) choosen. Lower value of weld hardness & HAZ hardness is most desired. So the quality characteristic for those responses are “ Lower the best” choosen Eqn (2) . Based on the above formulae S/N ratios were calculated and tabulated in Table 4. Table 4. S/N ratios of the responses Factors Experim ent No. 1 2 3 4 5 6 7 8 9
A Current (A) 475 475 475 525 525 525 600 600 600
B Voltage (V) 26 30 34 26 30 34 26 30 34
S/N ratio C Welding speed (mm/min) 300 400 500 400 500 300 500 300 400
Tensile strength 39.79 27.52 34.54 24.48 26.54 28.01 29.12 23.02 29.09
weld Hardness
HAZ hardness
-45.62 -45.11 -45.44 -45.11 -45.44 -45.30 -46.11 -46.32 -46.49
-45.44 -45.44 -45.11 -45.44 -45.30 -45.44 -46.49 -45.80 -46.11
B.Venugopal Rao/ Materials Today: Proceedings 5 (2018) 26961–26967
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5.1 Effect of process parameters on Tensile strength Regardless of the Quality characteristics, the higher S/N ratio gives the optimum condition. Accordingly from the Figure 1, it is observed that Current at Level I, voltage at Level III and Welding speed at Level I are having higher S/N ratio. As such the optimum parameter for turning is A1B3C1. The Corresponding values are Current 475 A, voltage 34 v and welding speed is 300 mm/min.
Fig. 1. S/N Ratios Vs Factors Table 5 ANOVA table for Tensile strength Factors current voltage welding speed Error Total
DOF
Sum of squares
Variance
F
2 2 2 2 8
0.29 2.30 0.53 0.33 3.44
0.14 1.15 0.27
0.87 7.04 1.63
Percentage contribution 8.29 66.75 15.48 9.48 100.00
From the ANOVA Table 5, it is observed that the most significant factor affecting Tensile strength is Voltage with a contribution of 66.75%. 5.2 Effect of process parameters on Weld Hardness Regardless of the Quality characteristics, the higher S/N ratio gives the optimum condition. Accordingly from the above graphs shown in fig.2 it is observed that Current at Level II, voltage at Level I and Welding speed at Level II are having higher S/N ratio. As such the optimum parameter for turning is A2B1C2. The Corresponding values are Current 525 A, voltage 26 v and welding speed is 400 mm/min
Fig. 2 . S/N Ratios Vs Factors
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B.Venugopal Rao/ Materials Today: Proceedings 5 (2018) 26961–26967
Table 6. ANOVA table for Weld Hardness
Factors
DOF
Sum of squares
2 2 2 2 8
1.90 0.03 0.05 0.18 2.16
Current Voltage Welding speed Error Total
Percentage contribution
Variance
F
0.95 0.02 0.02
10.31 0.17 0.26
87.82 1.42 2.25 8.51 100.00
From the ANOVA Table No. 6 , it is observed that the most significant factor affecting Weld hardness is Current with a contribution of 87.82 %. 5.3 Effect of process parameters on Heat Affected Zone Hardness Regardless of the Quality characteristics, the higher S/N ratio gives the optimum condition. Accordingly from the graphs shown in fig. 3 it is observed that Current at Level I, voltage at Level II and Welding speed at Level I are having higher S/N ratio. As such the optimum parameter for turning is A1B2C1. The Corresponding values are Current 475 A, voltage 30 v and welding speed is 300 mm/min
Fig. 3. S/N Ratios Vs Factors Table 7. ANOVA table for HAZ hardness Factors Current Voltage Welding speed Error Total
DOF 2 2 2 2 8
Sum of squares 1.20 0.13 0.02 0.17 1.52
Variance
F
0.60 0.07 0.01
6.97 0.77 0.10
Percentage contribution 78.87 8.75 1.08 11.31 100.00
From the above ANOVA Table 7, it is observed that the most significant factor affecting HAZ hardness is Current with a contribution of 78.87 %.
B.Venugopal Rao/ Materials Today: Proceedings 5 (2018) 26961–26967
26967
5.4 Microstructure analysis of Weld bead
Fig. 4 Microstructure of weld bead
The microstructures are studied in different zones like base metal, heat affected zone and weld zones. From the Figure 4 , the microstructure of the center of weld zone is found to be different from the heat-affected zone. The base microstructure consists of pearlite and ferrite banding in the matrix. The heat affected zone (HAZ) consists of coarse grain boundary ferrite, Widmanstatten ferrite and acicular ferrite in the matrix. The weld zone consists of network of columnar grains of grain boundary ferrite, acicular ferrite and Widmanstatten ferrite in the matrix. The hardness values of weld zone and heat affected zone (HAZ) didn’t follow any specific pattern, however the values are found to be in the similar range. 6.Conclusions From the analysis it is inferred that the optimal process parameters for Weld bead Tensile strength are Current (475A), Voltage (34 V) and Welding speed (300 mm/min), Optimal parameters for Weld hardness are Current (525A), Voltage (26 V) and Welding speed (400 mm/min) and Optimal parameters for HAZ hardness are Current (475A), Voltage (30 V) and Welding speed (300 mm/min). Voltage is found to be most influencing factor in deciding Tensile strength and Current is found to be most influencing factor in deciding Weld hardness and Heat affected zone hardness. References
[1] [2] [3] [4] [5] [6]
Edwin, Kumanan, optimization of submerged arc weld using non conventional techniques, Applied soft computing 11 (2011)5198-5204. Prasanta,Tapan, Sujit, Prediction of mechanical properties in submerged arc weld metal of C-Mn steel, Material and Manufacturing processes, 22: 114-127,2007 Ankush Batta, J K Agarwal Varinder Khurana, Amarjeet singh sandhu ,Optimisation of Submerged Arc Welding Process: A Review, IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE), Volume 12, 39-44,2015. Pratik Umrigar, Prof. Sandip J. Chaudhry, Parametric Optimization of Submerged Arc Welding On Stainless Steel- 304, International Journal for Technological Research in Engineering, Volume I, 2347-4718,2014. Amrat M. Patel Dr, Ashish V Gohil , International Journal of pure and applied research in Engineering and Technology, Review Article, Volume 1 (6),2013.. Tomasz Kozak, Submerged arc welded with an increased efficiency, Welding International, Vol 29, 614-618, 2015.