WELD BEAD GEOMETRY AND SHAPE RELATIONSHIP IN SUBMERGED ARC WELDING USING RECYCLED SLAG

International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) ISSN (P): 2249–6890; ISSN (E): 2249–8001 Vol. 10, Issue 3, Jun 2020, 597-610 © TJPRC Pvt. Ltd.

WELD BEAD GEOMETRY AND SHAPE RELATIONSHIP IN SUBMERGED ARC WELDING USING RECYCLED SLAG DEEPANJALI NIMKER & REETA WATTAL Scholar, Department of Mechanical Engineering, Delhi Technological University, Delhi, India ABSTRACT Submerged Arc Welding (SAW) is the most versatile method in the Automation industry which is diligently working on the concept of waste utilization and energy saving. During SAW, the flux melts and converts into non-biodegradable product known as slag. This slag is then recycled and thus proves to be an important measure in reducing waste. Slag consumption will not only prevent environmental pollution but also proves economical. When additives/ deoxidisers and binders were added to slag, it rejuvenates the workability, mechanical properties, metal integrity and bead quality of the welded specimen. In this paper, an attempt has been made to formulate slag equivalent to fresh flux and then pertained to study its effects on bead geometry shape relationship of mild steel bead on plate. For this, Response Surface Methodology (RSM) technique was adopted and mathematical models were developed to relate the input parameters (wire feed rate, arc voltage, travel speed and nozzle to tip distance) with response parameters (such as bead width, bead height, bead checked for adequacy using ‘F’ test and ‘t’ test, respectively. Adequacy test revealed that newly designed slag proves beneficial in presaging the weld bead quality. Also, the RSM approach helps in selecting optimum process controllable variables.

KEYWORDS: Slag Recycling, RSM, WPSF & WRFF

Original Article

penetration, weld penetration shape factor {WRSF} and weld reinforcement form factor {WRFF}). These models are then

Received: Mar 01, 2020; Accepted: Mar 21, 2020; Published: May 21, 2020; Paper Id.: IJMPERDJUN202055

INRODUCTION Now-a-days, the aim of automation industry is to recycle and utilize all their waste products in order to endure in this environment. SAW is contributing towards the concept by the use of slag as fluxduring welding. Recycling of slag was first attempted by using closed loop process [1] followed by the process of slag reclaimation[2]. Later, slag was recycled by adding deoxidisers and binderto it. This agglomerated slag was then subjected to welding where the physical and chemical properties of weld metal were evaluated and compared with welded specimen obtained using fresh flux. It was observed that weld chemistry, mechanical properties, microstructure and weld bead geometry were in accordance with AWS standard and thus, yields satisfactory performance [3-6]. Similarly, effect of recycled slag was investigated on stainless steel claddings and contributes to high quality weld bead along with cost saving upto 73% [7] and 70.73% in another research while welding mild steel plates [8]. During welding, there are many factors which affect the bead geometry of weld metal out of which current, electrode polarity, electrode diameter and electrode extension primarily affect the bead width, bead reinforcement and weld penetration. In this study, it was found that by the application of high current, straight polarity small electrode diameter and longer electrode extension it is possible to rise in melting rate of SAW [9]. Kiran et. al. [10] also reported the influence of wire current, current pluses and welding speed on bead geometry of HSLA steel and therefore concluded that the former two factors affect the weld bead width and reinforcement whereas weld bead penetration is affected by wire

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current. In addition, Murugan and Gunaraj [11] also investigated that rate of heat input and welding speed is inversely related, and thus enhances mechanical properties of weld metal. For this, mathematical models have been formulated to control process variables such as voltage, wire feed rate, welding speed and nozzle to plate distance to predict weld bead geometry shape relationship in graphical form and assessed that it is possible to control weld bead quality by appropriate process variables selection. Another approach includes the application of desirability function designed by Derringer and Harrington [12] to optimize weld parameters such as weld bead form factor, dilution, microhardness and diffusible hydrogen content in structural pipes. This approach analysed that arc voltage is varying proportionally with respect to all the parameters except microhardness but welding current plays the most important role in controlling welding parameters.

Similarly, another researcher suggested optimum weld pool geometry by mathematical model based on box behnken design in pulsed current tungsten inert gas welding (PCTIG) of titanium alloy and noticed that it is possible to relate weld bead dimensions with pulse current parameters using mathematical expressions [13]. Another theory, sensitivity analysis plays a pivotal role in this research. Karaoglu and Secgin[14] utilised the analysis and explored that the parameters such as welding current, voltage and welding speed can be fine tune to obtain ideal weld bead geometry using multiple curvilinear regression analysis. They penetration is almost non sensitive to the voltage and speed variation. Further, this concept was used to observe various effects of weld bead factors on weld bead volume. Result proves that sensitivity analysis is useful in controlling weld bead volume and improving weld quality [15]. Comparative study was carried out to predict and analyse the bead on plate and bead on joint weld plates for given region of HAZ. This study shows that bead on plate HAZ is larger as compare to that bead on joint but effect of process variables is same in both the cases [16]. Moreover, experiments were performedon ASTM A709 grade 50 steel plates in order to determine the consequences of heat input on bead geometry, contact angle, HAZ size, deposition area, penetration area and total molten area. It was revealed that all the parameters vary proportionally except bead contact angle. Also, plate melting efficiency and percentage dilution vary less as compare to electrode melting efficiency which increases and later decreases with the rise in heat input [17]. The factors affecting the flux consumption were current, voltage, wire diameter and polarity during twin welding whereas welding speed, contact tube to workpiece distance plays an important role with electrode negative [18]. It was also found that varying wire diameter can affect the consumption predominantly which then analyzed and compared heat source model parameters for twin wire with that of single wire welding [19]. Further, the effect of welding current, electrode polarity and wire diameter on size of heat source model was studied. It was concluded that these models are effective for estimating welding parameters in addition to that flux consumption also plays a significant role. Another researcher used oval shape heat source model to predict HAZ width, transient temperature distribution and weld pool on mild steel plate along with microstructural study. It was shown that there occurs a fair agreement in predicted and actual thermal field. Moreover, microstructure investigation presents higher grain growth in HAZ and fine equiaxed grains in the fusion zone [20].

Further, few researchers estimated the effect of various welding process variables on weld bead parameters but their work was not relevant to yield satisfactory results. Therefore, it is essential to design manufacturing process which produces desirable weld quality through optimal parameter combination without affecting welding output characteristics.

Datta and Kumar [21] evaluated the optimal condition for parameters to obtain agreeable weld bead quality on mild steel plates by using fused slag with fresh flux. Experiments were conducted using four levels full factorial method with welding current, slag mix percentage and flux basicity index as input variables. It was concluded that RSM technique proves reliable for parametric optimization when used along with multiple linear regression method whereas similar technique applied using five level central composite design [22] and also used to analysed the effect of polarity on weld bead Impact Factor (JCC): 8.8746

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geometry by predicting the its critical dimensions at straight and reverse polarity [23]. Also, grey relational analysis was used with Taguchi method which gives optimal results. Similarly, another researcher utilized taguchi method with regression analysis to investigate the relation between input output variables and to optimise them by GA and PSO. The results obtained were compared and therefore found acceptable [24]. Another approach towards weld bead geometry optimization was through weighted principal component analysis which introduce multi response performance index to attain single objective function by using Taguchi L25 orthogonal array design. Confirmatory test shows that the proposed method is highly effective to obtain optimal results [25]. Kolahanet. al.[26] performed experiments using regression analysis to evaluate input output relation in welding by mixture of fresh flux and fused slag. They have used SA for parameter optimization which was found to be very effective for obtaining desired weld bead characteristics. Furthermore, mathematical models were developed by Taguchi method based on multi regression and artificial neural network. This model was represented graphically and thus proves capable of selecting optimal welding parameters [27-28]. Moreover, ANN was used Kumananet. al. [29] developed mathematical model based on Taguchi method with multiple regression analysis using statistical package for social science to predict weld bead geometry for any welding condition. This method proves beneficial for optimal process parameter selection. In another work, ANN was utilized to model shielded metal arc welding to show relation between welding variables and weld bead geometry. Results revealed a negligible difference between estimated and experimental values.

METHODS Plan of investigation consist of following steps: Collection of Slag from Industry Slag was collected from the dump yard of Cheema boilers industry kurali, punjab. This slag was produced during the submerged arc welding process using F7 A4 EH14 flux wire combination.

Designing Slag by the Process of Recycling Slag once collected was washed thoroughly with tap water. It was then dried and crushed into powder form using ball mill.

Figure 1 shows ball mill used to crush slag material. Figure 2 shows the balls along with slag inside the ball mill.This powdered slag is then mixed with alloying elements, deoxidisers and binder to it. This wet mixture was passed through 12 mesh size screen to convert into small pellets. These pellets were air dried for 24 hours and then baked in stir casting furnace at 850o C for two hours [33]. Once baked, it was crushed to granular size equivalent to fresh flux. This slag formulated is known as recycled slag. Figure 3 represents recycled slag and fresh flux.

Welding with Recycled Slag and Fresh Flux For evaluating the chemical composition of weld metal, weld pads were prepared using recycled slag and pure flux. This weld pad was having three beads per layer and four layers high [33]. Figure 4 (a) and (b) shows weld pad using recycled slag and fresh flux, respectively. The welding conditions were as per ASME SFA 5.17 [33]. The transverse section welded sample having approximately 50 mm length were removed for determing chemical composition. The uppermost layer of weld metal was carried out by spectrometer.

Comparison of Chemical Composition by Spectro Analysis Table 1 shows chemical composition of weld pad obtained using recycled slag, flux and AWS standard values. It was www.tjprc.org

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observed that reclaimed slag has similar composition as that of fresh flux and also in accordance with AWS standards.

Identifying the Important Factors and Determining their Upper and Lower Limits Four independently controllable parameters were identified as wire feed rate, arc voltage, travel speed and nozzle to plate distance. For determining their working range, trial runs were conducted by varying one parameter and keeping others constant. Table 2 shows the welding input with their upper and lower limit. The coded values for intermediate values were then calculated from the relationship [31]:

Where, Xiis the required coded value of a variable X, X is any value of the variable from Xminto Xmax; Xmaxand Xminare the maximum and minimum levels of the variables Developing the Design Matrix Design matrix was represented for two level Half factorial design where column and row corresponds to levels of factors for eight distinct trial runs.

Conducting the Experiments as Per Design Matrix using Recycled Slag As per design matrix, bead on mild steel plate having dimensions as mm were deposited using recycling slag with 3.15 mm EH14 wire. In order to avoid systematic error, experiments were conducted randomly. Figure 5 shows submerged arc welding machine. Eight sets of experimental runs after welding were shown in figure 6.

Observing the Response Parameters Once the experiments were conducted, the welded specimens were removed from the middle of mild steel plates. These specimens were then polished followed by etching with 2% nital solution. The weld bead geometry i.e. bead width, bead height and penetration were calculated and shown in Table 2.

Formulating the Mathematical Models The response function can be expressed as: Y = f (V, F, S,D) Where, Y- Response parameter, V - Arc Voltage, F - Wire feed rate, S - Travel speed, D - Nozzle to tip distance Assuming linear relationship, the possible two factor interactions are taken into consideration, therefore the above expression rewritten as: Y = b0 + b1V+ b2F + b3S+ b4D + b12VF + b13VS + b14VD + b23FS + b24 FD + b34 SD After confounding the given mathematical model can be expressed as: Y = b0 + b1V+ b2F + b3S+ b4D + b5 (VF+SD) + b6 (VS+FD) + b7 (VD+FS)

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valuating the Coefficients of the Polynomials Coefficients of the mathematical model were evaluated according to the design matrix. This is based on the method of least squares which is as follows: bj =

, j = 0,1……k

Where, Yi = value of the response parameter,Xji= value if a factor in coded form, N = No of runs, K = No of coefficients of the model The significance of each coefficient was tested using ‘t’ test. Here the value of‘t’ from the standard table is 2.3 for confidence level 95% and degree of freedom as 8. Table 3 describes the model coefficients and their significance. ‘t’ value can be expressed as following:t = Where bj= Absolute Value of Coefficient and Sbj = Standard Deviation of Coefficient Sbj =

Where

= Variance of Optimization Parameter

Evaluating the Coefficient’s Significance and Obtaining the Final Model The design matrix developed was used to determine the coefficients of model, substituting the values of these coefficients in equation 3, we obtain final developed model which is given in Table 4.

Analysing the Adequacy of Mathematical Models Analysis of variance technique was used to analyze the adequacy of mathematical model. According to this technique, the model is significant when calculated model’s F-ratio value is less than tabulated value for a desired level of confidence at 95% confidence level having 4 as degree of freedom.It has been found that all models are significant as shown in Table 6.

Mathematical expression of F ratio of the model is given below: F model =

Where

= Variance of adequacy

Graphical Representation of the Effect of Process Parameters on Weld Bead Geometry Figure 8 to 42 shows graphs as well as contour plots to depict the effect of various input factors on bead geometry shape relationship parameters.

Figure 1: Ball mill to Crush Slag. www.tjprc.org

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Figure 2: Slag Crushed in Ball Mill.

Figure 3: Recycled Slag and Fresh Flux.

(a) (b) Figure 4: Weld Pad using (a) Recycled Slag (b) Fresh Flux.

Figure 5: Submerged Arc Welding Machine.

Figure 6: Eight Sets of Experimental Runs after Welding.

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RESULTS AND DISCUSSIONS

Figure 7: Weld Bead Profile of Eight Sets of Runs.

Table 1: Welding Input with their Upper and Lower Limit Low Parameters Units Symbol High (+1) (-1) Volts V 25 40 Arc Voltage Wire Feed cm/min F 97 156 Rate cm/min S 32 40 Travel speed mm D 18 23 NTD

Table 2: Bead Geometry Run

V

F

S

D

Bead Width (mm)

Bead Height(mm)

1 2 3 4 5 6 7 8

+ + + +

+ + + +

+ + + +

+ + + +

14 13 17 19.5 13 14.1 16.5 18

2.03 2 2.5 2 2.86 2.11 2.53 2.4

Response Parameters

Degree of Freedom

Bead width Bead height Bead penetration WPSF WRFF

Table 3: Test for Adequacy Variance St.Devtn Variance of of of Optimisation Coefficient Adequacy Parameter Sbj 0.88 0.33 9.88 0.017 0.046 0.16

WPSF

WRFF

5.0 7.64 4.04 3.9 5.65 5.64 3.60 5.14

6.89 6.5 6.8 9.75 4.54 6.68 6.52 7.5

FRatio Model

Fratio From Table

Model Whether Significant

11.23 9.70

9.12 9.12

Yes Yes

4 4

3 3

4

3

0.12

0.12

2.31

18.57

9.12

Yes

4 4

3 3

0.29 0.22

0.19 0.16

2.75 3.44

9.31 15.90

9.12 9.12

Yes Yes

Parameter

Coefficient

Bead Width

value ‘t’ value significant

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Bead Penetration (mm) 2.8 1.7 4.2 5.0 2.3 2.5 4.5 3.5

Table 4: Coefficients of the Model b1 b2 b3 b4 b0 b5 (VF) (V) (F) (S) (D) 15.63 0.51 2.11 -0.23 -0.38 0.48 47.63 1.54 6.39 0.69 1.15 1.45 Yes No Yes No No No SCOPUS Indexed Journal

b6(VS)

b7 (VD)

-0.25 0.75 No

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Bead height Bead Penetration WPSF

WRFF

value ‘t’ value significant value ‘t’ value significant value ‘t’ value significant value ‘t’ value significant

S.No 1 2 3 4 5

Figure 8

Figure 13

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2.30 50 Yes 3.31 27.58 Yes 5.07 26.68 Yes 6.89 43.06 Yes

-0.17 3.69 Yes -0.13 1.08 No 0.50 2.63 No 0.71 4.43 Yes

0.05 1.08 No 0.98 8.16 Yes -0.90 4.73 Yes 0.74 4.62 Yes

0.17 3.69 Yes -0.11 0.91 No -0.06 0.31 No -0.58 3.62 Yes

0.13 2.82 Yes -0.38 3.16 Yes 0.54 2.84 Yes -0.56 3.5 Yes

0.01 0.21 No 0.08 0.66 No -0.15 0.78 No 0.27 1.68 No

-0.06 1.30 No -0.18 1.5 No 0.26 1.36 No -0.04 0.25 No

-0.04 0.86 No -0.06 0.50 No -0.12 0.63 No 0.07 0.43 No

Table 5: Developed Models for Weld Bead Geometry Bead Geometry Factors Developed Model Bead Width 15.63 + 2.11 F Bead height 2.30 – 0.17 V + 0.17 S + 0.13 D Bead Penetration 3.31 + 0.98 F – 0.38 D WPSF 5.07 – 0.90 F + 0.54 D WRFF 6.89 + 0.71 V + 0.74 F – 0.58 S – 0.56 D

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Figure 38 (a)

Figure 39 (a)

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Experimentation were carried out according to the half factorial design to analyse the effect of independent variable such as arc voltage, welding current, wire feed rate and nozzle to tip distance on the responses, for instance bead width, bead height, bead penetration, WPSF and WRFF, and the outcomes are demonstrated above in Table 2.

To check the adequacy of model, linear regression model should be suggested, and for this variance of optimization parameter, variance of adequacy, standard deviation of coefficient were calculated in order to find F value.

Tabulated F value is 9.12 for degree of freedom and confidence interval as 4 and 95% respectively. As per this model, if calculated F-value is greater than tabulated value F-value then the model was significant. It was observed in Table 3 and 4 that F and t value for given responses were adequate within the range of defined parameters. Using this model, the final equations obtained in actual factors which are used to forecastthe output, these equations are given in Table 5.

Based on the diagnostics of model adequacy various plots such as normal plot of residuals vs internally studentized, predicted vs actual, DFFITS vs run, Leverage vs run, DFBETAS vs run and residual vs run were plotted in order to test the goodness of fit of model. Figure 8, 14, 20, 26 and 32 shows a scatter plot where normal plot on y axis and internally studentised plot on x axis. These figures shows evenly distributed error terms along 45o which suggests a linear relation within set of observations. Figure 9, 15, 21, 27 and 33 shows the relation between predicted and actual values obtained from the model. These points were close to the fitted line which indicates a good fit model with narrow confidence interval. To measure the influence of model terms on fitted term, DFFITS was represented with respect to the number of runs in Fig. 10, 16, 22, 28 and 34 which reveals that the points lie within the range i.e. +2.37 to – 2.37 whereas all the figures except Fig.34 reported that there occurs two points which lie outside the defined range. These points have negligible effect on model accuracy. Moreover, Fig. 11, 17, 23, 29 and 35 shows that all the leverage points lie on horizontal estimated regression line at 0.625 which is less than unity and elaborates the deviation of independent variable from mean. On the other hand, Fig. 12, 18, 24, 30 and 36 depicts the relation between DFBETAS and run which explains the impact of particular term on each estimated regression factors. These figures demonstrates that DBETAS values lies within range i.e. +1.06 to -1.06, therefore gives an adequate model. Lastly, internally studentized residuals plotted against eight set of runs were described in figures 13, 19, 25, 31 and 37 this graphical representation illustrates correlation between the error terms that are near to each other, here the terms tends to evenly distributed above and below regression line=0.

Thus suggests a linear relationship among residual values.

Apart from above mentioned graphs, there occurs another technique for given set of values i.e. response surface technique. This technique optimizes those responses which are affected by the process variables. Moreover, it evaluates the

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relationship between input variables and attained response parameters [31]. However, in order to determine the optimal condition that produce maximum or minimum level of response, the graphs were plotted and shown in fig. 38 to 42. These plots shows the effect of various parameters such as Arc voltage, wire feed rate, travel speed and nozzle to tip distance on responses such as bead width, bead height, bead penetration, WPSF and WRFF. Figure 38 a, b and c shows interaction effect of arc voltage with wire feed rate, travel speed and nozzle to tip distance on bead width. It was observed that bead width increases from 12.98 to 19.5 mm with the increase in values of all response parameters except travel speed where it remains constant. Figure 39 a, b and c shows interaction effect of arc voltage with wire feed rate, travel speed and nozzle to tip distance on bead height. Bead height depicts similar variation with each input variable i.e. it decreases with an increase in responses. Figure 40 a, b and c shows interaction effect of arc voltage with wire feed rate, travel speed and nozzle to tip distance on bead penetration. Graphs plot in these figures explains the variation of rising bead penetration with input parameters of same behaviour. Figure 41 a, b and c shows interaction effect of arc voltage with wire feed rate, travel speed and nozzle to tip distance on WPSF. Figure 41 a and c elaborates that bead penetration has linear relation with wire feed rate, nozzle to tip distance and arc voltage whereas it has inverse relation with travel speed. Figure 42 a, b and c shows interaction effect of arc voltage with wire feed rate, travel speed and nozzle to tip distance on WRFF. It was found that response factor variates positively with input parameters except wire feed rate.

CONCLUSIONS In this research paper, slag recycling and the importance of RSM were discussed. Half factorial four factors method was applied for investigation. Mathematical model equations were obtained, using design expert 10 software for submerged arc welding. 

Predicted values were found in fair agreement with actual values obtained by conducting experiments.



RSM technique could be applied efficiently for process optimization of weld bead geometry as well as better way to obtain data with lesser number of runs.



All the responses viz. bead width, bead height, bead penetration, WPSF and WRFF increase with the increase in Arc voltage.



All the responses viz. bead width, bead height, bead penetration and WRFF increase with the increase in wire feed rate except WPSF.



All the responses viz. bead width, bead penetration, WPSF and WRFF decrease with the increase in travel speed except bead height.



All the responses viz. Bead penetration, bead height WPSF and WRFF increase with the increase in nozzle to tip distance except bead width.



Mathematical models can be easily employed and effective in acquiring desirable weld bead characteristics.



Slag designed by reclaimation found to be in accordance with AWS Standard.



Slag can be reclaimed and used profitably.

ACKNOWLEDGEMENT

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The authors acknowledge the support of Department of Mechanical, Industrial & Production, Automobile Engineering of the Delhi Technological University, New Delhi, India for imparting the prerequisite to conduct the experiments.

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