Modeling and analysis by response surface methodology of hardness for submerged arc welded joints using developed agglomerated fluxes

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1 Indian Journal of Engineering & Materials Sciences Vol. 19, December 2012, pp Modeling and analysis by response surface methodology of hardness for submerged arc welded joints using developed agglomerated fluxes Ajay Kumar a *, Hari Singh b & Sachin Maheshwari c a Noida Institute of Engineering & Technology, Greater Noida , India b National Institute of Technology, Kurukshetra , india c Netaji Subhas Institute of Technology, New Delhi , India Received 11 June 2012; accepted 20 November 2012 The present study evaluates the hardness of submerged arc welded joints by using developed agglomerated fluxes during submerged arc welding. Response surface methodology (RSM) technique is used to conduct the experiments. Flux constituents MnO, CaF 2, NiO, MgO and Fe-Cr are chosen as variables added into the main constituents CaO, SiO 2 and Al 2 O 3 to study the performance in terms of hardness. The results identify the most important constituents favoring the hardness are MnO, MgO, NiO and Fe-Cr, where as CaF 2 is not so prominent to improve the hardness. Keywords: Response surface methodology, Submerged arc welding, Agglomerated fluxes, Hardness The submerged arc welding (SAW) process is a versatile process; therefore, it is used for wide range of applications including the critical application, e.g., joining of pressure vessels, thick plates, ship hulls etc. In submerged arc welding, the arc is covered by flux. The constituents present in the flux controlled arc stability, bead shape and mechanical and chemical properties of weld metal 1. The SiO 2 content in the synthetic fluxes improves the welding performance. It is determined through metallographic examination that the presence of a metallic mist in the matrix of the flux and the amount of metallic mist in the flux increased with increasing silica content 2. Colvin 3 in his study revealed that the basic fluxes are associated with the improved metallurgical properties of weld than the acidic fluxes. The study of commercial acidic fluxes by Bennett has correlated the slag detachability and weld-bead surface appearance to their chemical composition 4. Ferrera and Olson 5 studied the performance of MnO-SiO 2 -CaO flux with respect to viscosity, arc stability, and weld-bead morphology. The flux behavior, flux chemistry and slag/metal interactions were reported elsewhere Terashima and Tsuboi 11 used highly basic agglomerated flux containing adequate amounts of carbonate and fluoride which produce low hydrogen weld metal. *Corresponding author ( ajayagrohi@gmail.com) Tandon et al. 12 designed CaO-TiO 2 fluxes with the minor additions of Al 2 O 3, MgO and CaF 2, and revealed their physical properties. Burck et al. 13 studied the effects of CaF 2, CaO and FeO addition on weld metal chemistry. The various studies have revealed the effect of various flux constituents on to the transfer of elements to the weld metal and as a result the mechanical and metallurgical properties of the weld metal get affected The hardness being the most important property to be imparted to the weld metal is proposed to be studied in the present work. Experimental Procedure CaO-Al 2 O 3 -SiO 2 based flux systems have been selected for study as these are the most widely used fluxes at the commercial level. The ranges of these constituents were designed on the basis of binary and ternary phase diagrams for different oxide and fluoride systems. The details of basic constituents, alloying constituents and their ranges are given in Table 1. After ascertaining their ranges, fluxes were prepared by agglomeration method. The fixed process parameters of submerged arc welding were selected on the basis of good slag detachability, weld appearance, arc initiation, and arc stability. Experiments were conducted on low carbon steel plates of size mm (V-groove at 45 ) by

2 380 INDIAN J. ENG. MATER. SCI., DECEMBER 2012 Table 1 Flux constituents and their ranges Basic constituents & CaO SiO 2 Al 2 O 3 MnO CaF 2 MgO NiO Fe-Cr alloying constituents Amount Table 2 Chemical composition of welding electrode and base plate Composition of electrode and base plate Electrode (Auto Rod 12.08L 3.15 mm) EL-8 (ESAB) Low carbon steel plate C Mn Si Ni Cr using developed agglomerated fluxes during submerged arc welding. All the weld joints were made with a flux depth and electrode stick-out was 25 mm. Welding conditions were set approximately 500 A and 36 V DC electrode positive at 280 mm/min travel speed, with a wire diameter of 3.15 mm, giving a heat input of about 3.87 kj/mm. The chemical composition of submerged arc welding electrode and base plate are given in Table 2. The Vickers hardness of these welded joints was measured on Vickers hardness testing machine by applying 30 kgf load for 10 s on the prepared specimens and took three readings at different places on the same specimen. Response Surface Methodology (RSM) Experiments were planned on the basis of response surface methodology (RSM) technique. The designexpert trial version software was used for central composite second order rotatable design with five variables as flux constituents for the development of agglomerated fluxes during submerged arc welding. The 2 5 factorial points (16 at half replication), plus 10 star points and plus 6 centre points give total 32 experiments 21 given in Table 3. Variables MnO, CaF 2, NiO, MgO and Fe-Cr and their levels are given in Table 4. Each weld joint was performed, using different flux, with the fixed welding parameters. The response surface methodology (RSM) was used to design and point out the relationship between the response of interest and the variables (alloying constituents of fluxes) as well as to determine the conditions of these variables to optimize this No. of experiment Table 3 Design matrix in coded form MnO CaF 2 MgO NiO Fe-Cr Table 4 Flux constituents used in the experiment and their levels Variable MnO CaF MgO NiO Fe-Cr

3 KUMAR et al.: RESPONSE SURFACE METHODOLOGY 381 response 22,23. In this study, five constituents of flux taken as variables were used as levels that maximize the yield (Y) of a process and it is shown in the equation, represented by: Y= f (x 1, x 2, x 3, x 4, x 5 ) ± є (1) Where Y is the response, f is the response function, є represents the experimental error, and x 1, x 2, x 3, x 4, and x 5 are independent constituents in the developed agglomerated flux. The expected response (hardness) is defined by E(Y) = f (x 1, x 2, x 3, x 4, x 5 ) = η, where η is called a response surface. In response surface methodology, response surface function f establishes the relationship between response and the independent variables and the form of f is unknown. The experimental design and the results were analyzed using a first-degree polynomial equation: Y = β 0 + β 1 X 1 + β 2 X β 5 X 5 ± є (2) The behavior of the system can be described by the following second-order polynomial equation: i= 5 i= 5 2 o i= 1 i i i= 1 ii i ij i j Y = β + β x + β x + β x x (3) Where Y is the response, β o is the interception coefficient, β i are the linear terms, β ii are the quadratic terms, β ij are the interactions terms, and x i and x j are the coded levels of the independent variable taken in the form of flux constituents. Table 5 gives the design matrix of agglomerated fluxes constituents and response in terms of Vickers hardness numbers. Results and Discussion The analysis of variance (ANOVA) and pooled ANOVA reported in Tables 6 and 7 show that the model is significant. The f-value of and the Table 5 The design matrix of agglomerated fluxes constituents and response in terms of Vickers hardness numbers Flux Code Run MnO CaF 2 MgO NiO Fe-Cr Vickers hardness (VHN) AGF AGF AGF AGF AGF AGF AGF AGF AGF AGF AGF AGF AGF AGF AGF AGF AGF AGF AGF AGF AGF AGF AGF AGF AGF AGF AGF AGF AGF AGF AGF AGF

4 382 INDIAN J. ENG. MATER. SCI., DECEMBER 2012 Table 6 ANOVA of Vickers hardness Source Sum of squares Degree of freedom Mean square f-value Prob > f Model < significant A-MnO B-CaF C-MgO D-NiO E-Fe-Cr AB AC 1.137E E E AD AE BC < BD BE CD CE DE A B C D E Residual Lack of fit not significant Pure error Cor total Std. dev. = 1.72 R-squared = Mean = Adj R-squared = C.V. % = 1.05 Pred R-squared = Press = Adeq precision = Table 7 Pooled ANOVA of Vickers hardness Source Sum of squares Degree of freedom Mean square f-value Prob > f Model < significant A-MnO B-CaF C-MgO D-NiO E-F-Cr AD AE < BC < DE A < Residual Lack of fit not significant Pure error Cor total Std. dev. = 1.56 R-squared = Mean = Adj R-squared = C.V. % = 0.95 Pred R-squared = Press = Adeq precision =

5 KUMAR et al.: RESPONSE SURFACE METHODOLOGY 383 Fig. 1 Normal probability plot of the residuals for hardness Fig. 2 Predicted hardness versus actual value hardness Fig. 4 Effect of CaF 2 and MgO on Vickers hardness Fig. 3 Effect of NiO and Fe-Cr on Vickers hardness Fig. 5 Effect of MnO and NiO on Vickers hardness

6 384 INDIAN J. ENG. MATER. SCI., DECEMBER 2012 p-value < did in fact demonstrate that this regression was statistically significant at 99% confidence level. Besides, the fit of the model was checked by the coefficient of determination R 2 which was found to be , thus pointing out that only 8.68% of the total variations in the response were not explained by the model. The associated p-value for the model is lower than 0.05 (i.e. α=0.05, or 95% confidence) which indicates that the model is considered to be statistically significant. The lack-of-fit term is not significant as it is desired. Further, factors MnO, MgO, Fe-Cr, interaction effect of factor MnO with factor NiO, interaction effect of factor MnO with factor Fe-Cr, interaction effect of factor CaF 2 with factor MgO, interaction effect of factor NiO with factor Fe-Cr and second-order term of factor MnO, have significant effect. To fit the quadratic model for hardness, the non-significant terms are eliminated. The ANOVA Table 6 for the reduced quadratic model for hardness is shown in Table 7. The reduced model results indicate that the model is significant (R 2 and adjusted R 2 are and respectively), lack of fit is not significant. After eliminating the non-significant terms, the final response can be represented by the equation: Vickers hardness = * MnO * CaF *MgO * NiO * Fe-Cr * MnO* NiO * MnO * Fe- Cr * CaF2 * MgO * NiO*Fe-Cr * MnO 2 (4) Figure 1 displays the normal probability plot of the residuals for Vickers hardness. It is noticed from this plot that the residuals are falling on a straight line, which means that the errors are normally distributed about the straight line. The actual values of Vickers hardness are compared with the predicted values for the model in Fig. 2. It can be seen that the regression model is fairly well fitted with the observed values. Figure 3 depicts the response surface for Vickers hardness with the interaction of flux constituents NiO and Fe-Cr. In 3-D figure hardness tends to increase with increase in the percentage of NiO because Ni element provides good hardness in NiO constituents. The hardness also increases with the increase of Fe-Cr because Fe and Cr are good transition metals. The effect of CaF 2 and MgO constituents on Vickers hardness is shown in Fig. 4. The figure shows that the value of Vickers hardness increases with CaF 2 constituent. As per Lewis 24 the techno-mechanical property slightly increases with increasing CaF 2 content, because it lowers the high oxygen content and many inclusions present in the submerged arc welds. The value of Vickers hardness also increases with the percentage of MgO, because it is very stable (non-reactive) with the combination of CaF 2 which reduces the oxide nature. Figure 5 shows the behavior of MnO and NiO constituents on Vickers hardness. The effect of NiO on hardness was also positive because the presence of Ni in NiO increases the hardness. The value of hardness also increases with the percentage of MnO constituents because it is a transition metal oxide. Figure 6 shows the response surface of Vickers hardness with the interaction of MnO and Fe-Cr. The value of hardness also increases with the percentage of MnO constituents because it is a transition metal oxide. The hardness also increases with the increase of Fe-Cr because Fe and Cr are good transition metals. Validation experiments The confirmation tests were performed for validation. For confirmation tests, the values of the constituents were selected from the given specified range and three experiments were performed for Vickers hardness. The experimental values obtained from confirmation tests were compared with the model predicted Vickers hardness. From the analysis of Table 8, it observed that the calculated error is small. Fig. 6 Effect of MnO and Fe-Cr on Vickers hardness

7 KUMAR et al.: RESPONSE SURFACE METHODOLOGY 385 Table 8 Confirmation tests and their comparisons with the predicted values Experiment No. MnO CaF 2 MgO NiO Vickers hardness (VHN) Error % Fe-Cr Experimental Predicted Conclusions From this study, it is concluded that the flux constituents influence the hardness of welded joint during submerged arc welding. The study revealed that the hardness increases with the wt% of constituents, i.e., MnO, CaF 2, MgO, NiO and Fe-Cr. The major change in hardness occurred due to MgO constituent because of the non reacting nature of the metal oxide. The CaF 2 constituent affects less on hardness as compared to other constituents because it reduced oxygen level due to interaction with SiO 2. The study is thus helpful to design the flux for the submerged arc welding applications to get the appropriate hardness of the welded joint. References 1 Jackson C E, Fluxes and slags in welding, Bulletin 190 (New York: Welding Research Council), Dec Butler C A & Jackson C E, Weld J, (April 1967) 448-s-455-s. 3 Colvin P, Metallurgia, (February 1970) Bennett A P, Met Constr Brit Weld J, (Dec 1970) Ferrera K P & Olson D L, Weld J, (July 1975) 211-s-215-s. 6 Wittstock G G, Weld J, (Sept 1976) North T H et al, Weld J, (March 1978) 63-s-75-s. 8 Chai C S & Eager T W, Weld J, (July 1982) 230-s -232-s. 9 Mitra U & Eager T W, Metall Tran A, 15A (1984) Lau T et al., Weld J, (Feb 1986) 31s-38-s. 11 Terashima H & Tsuboi J, Met Constr, (Dec 1982) Tandon S Kaushal G C & Gupta S R, Effect of flux characteristics on HAZ during submerged arc welding, Int. Conf. on welding Technology, University of Roorkee, India, September 1988, II-65-II Burck P A, Indacochea J E & Olson D L, Weld J, (March 1990) 115-s-122-s. 14 Paniagua Ana Ma et al., J Mater Process Technol, 169 (2005) Kanjilal P et al., J Mater Process Technol, 171 (2006) Kanjilal P et al., Weld J, 86 (2007) 135 -s -146-s. 17 Adeyeye A D & Oyawale Festus A, J Braz Soc Mech Sci Eng, XXX (4) (2008) Adeyeye A D & Oyawale Festus A, Mater Res, 12(3) (2009) Bang Kook-soo et al., Met Mater Int, 15(3) (2009) Kumar P et al., J Eng Manuf, 225 (2010) Cochran G & Cox G M, Experimental design, (Asia Publishing House, New Delhi), Douglas C M, Design and analysis of experiments, 5 th ed, (John Wiley), (2007) Box G E P & Draper N R, Evolutionary operation, (John Wiley, Newyork), Lewis W J et al., Weld J, (August 1961) 337-s -345-s.