EXPERIMENTAL INVESTIGATIONS ON FRICTION WELDING PROCESS FOR DISSIMILAR MATERIALS USING DESIGN OF EXPERIMENTS

Transcription

1 137 Chapter 6 EXPERIMENTAL INVESTIGATIONS ON FRICTION WELDING PROCESS FOR DISSIMILAR MATERIALS USING DESIGN OF EXPERIMENTS 6.1 INTRODUCTION In the present section of research, three important aspects are investigated using design of experiments (DOE). First, a new joint geometry was considered for investigation due to certain advantages as presented in chapter 4. Second, influence of weld parameters on weld interface temperature is investigated, as temperature is very critical in friction welding of Al 6061 and SS 304 due to tendency of intermetallic layer formation at weld interface and third, influence of weld parameters on tensile strength and upset are investigated. The experimental data for each of the responses (tensile strength, upset and temperature) are analyzed using analysis of variance (ANOVA) to identify the significant contributing factors. Main effect plots are presented to understand the influence of different weld parameters on responses. Interaction plots are presented to understand whether the effect of one factor depends on the level of other factor. Interaction plots are used to visualize possible interactions. Mathematical model is developed to predict tensile strength, interface temperature and upset. The relation

2 138 between friction welding process inputs and measured outputs helps in proper implementation of machine controls and it also helps in special purpose machine design. One factor at a time approach was not used for experimentation because one factor at a time strategy fails to consider any possible interaction between the factors and they are less efficient. 6.2 EXPERIMENTAL PROCEDURE Al 6061 and SS 304 are welded with direct drive friction welding setup with new joint geometry as discussed in chapter 4. The diameter and length of weld specimens are considered as 25 mm and 125 mm respectively. The design of experiment process is divided into three main phases that encompasses all the experimentation approach. The three phases are a) the planning phase b) the conducting phase and c) the analysis phase. The block diagram of DOE is shown in Figure 6.1. The important controllable process variables for friction welding process are rpm, friction time, forging time, friction pressure and forging pressure. The three levels of process parameters and trail details are given in Table 6.1 and Table 6.2 respectively. The uncontrollable variables such as alignment of parts, cleanliness of weld surfaces, surface finish etc. These uncontrollable variables are controlled for the purpose of test.

3 139 The strength of weld joint is evaluated by tensile test. The temperature developed at the weld interface is recorded with thermocouple. The upset is measured by data acquisition system. The experimental data for each of the response is analyzed using analysis of variance (ANOVA) to identify the significant contributing factors. Main effect plots are studied to understand the influence of weld parameters on tensile strength, upset and weld interface properties. Interaction plots are studied to understand the combined effect of two or more factors at a time. Mathematical model is developed to predict the tensile strength, upset and weld interface temperature. Figure 6.1 Block diagram of design of experiment S.No. Welding Parameters Low Medium High 1 RPM Friction Pressure (MPa) Friction Time (Seconds) Forging Pressure (MPa) Forging Time (Seconds) Table 6.1 Friction weld parameters and levels

4 140 Friction welding parameters Responses S.No. RPM FRP (Mpa) FT (Sec.) FOP (Mpa) FOT (Sec.) UTS (Mpa) Upset (mm) Temp ( C) Table 6.2 Friction welding trails Where FRP Friction pressure, FT Friction time, FOR Forging pressure, FOT Forging time, UTS Ultimate tensile strength

5 RESULTS AND DISCUSSIONS ANALYSIS OF VARIANCE Anova is a statistical technique used for analysing the data from the experiments. ANOVA compares the response variable means at the different factor levels to evaluate the importance of one or more factors. It tests the hypothesis whether the means of two or more populations are equal or not. The null hypothesis states that all population means are equal while the alternative hypothesis states that at least one is different. The experimental data for each of the response (tensile strength, upset and temperature) is analysed using ANOVA to identify the significantly contributing factors. The Analysis outputs tensile strength, upset and temperature is presented in the Table 6.3, Table 6.4 and Table 6.5 respectively. It includes the source of variation, their degrees of freedom and the total sum of square. The MS (mean-squares), F-statistics and p-values are also included into the ANOVA table to find out whether the predictors or factors are significantly related to the responses. The various components of ANOVA table are Source which represents the source of variation, either from interaction or the factor or the error. The total is a addition of all the sources. DF is degrees of freedom from each source. If a factor has three levels, the degree of freedom is 2 (n-1). SS represents sum of squares between groups (factor) and the sum of squares within groups (error). Mean squares (MS) are found by

6 142 dividing the sum of squares by the degrees of freedom. F F can be calculated by dividing the factor MS by the error MS; Compare this ratio against a critical F found in the table or use the p--value to find out whether the factor is significant or not. Typically compare against an alpha value of If the p-value is lower than 0.05, then the factor is significant ANALYSIS OF VARIANCE OF ULTIMATE TENSILE STRENGTH The Anova table for UTS is shown below Table 6.3 Source DF Seq SS Adj SS Adj MS F P RPM FRP FT FOP FOT Error Total Table 6.3 Analysis of variance for UTS, using adjusted SS for tests The percentage contribution of factors for ultimate tensile strength is given in phi chart as shown in Figure 6.2. Figure 6.2 Pie chart for percentage contribution for ultimate tensile strength

7 143 It is observed from Figure 6.2 that, friction time and forging pressure are the most significant factor and forging time has least impact on ultimate tensile strength. P value indicates the level at which the corresponding effects are considered to be significantly different and in general the p value have to be less than 0.05 to say the effect is statistically significant at 95% or more MAIN EFFECT PLOTS FOR ULTIMATE TENSILE STRENGTH The main effect plots for ultimate tensile strength in shown in Figure 6.3. Figure 6.3 Main effect plots for ultimate tensile strength Main effect plot is drawn by averages of observations at each linking. The steeper the slope of the line, the greater is the magnitude of the main effect. The main effect plots for the rpm, friction pressure,

8 144 friction time, forging pressure and forging time are placed together in one graph to compare their relative magnitudes. Ultimate tensile strength decreases as rpm increases. Lower rpm helps in minimizing the formation of intermetallic compound at the rubbing surfaces. Thus ensures good weld strength. Ultimate tensile strength decreases as friction time increases. Friction time is selected so as to ensure that the interface surfaces are cleaned by friction and temperature is raised to achieve the required plasticity for welding. With increase in friction time the heat at the interface increases and thus it may increases the tendency of intermetallic layer thickness and is responsible for lower ultimate tensile strength. With increase in friction pressure the ultimate tensile strength decreases up to 50 MPa then it increases. Ultimate ensile strength increases as forging pressure increases. Forging time has least effect on the ultimate tensile strength INTERACTION PLOTS FOR ULTIMATE TENSILE STRENGTH Interaction plots are studied to understand whether the effect of one factor depends on the level of the other factor. Interaction plots are used to visualize possible interactions. Parallel lines in an interaction plot indicate no interaction. The greater the difference in slope between the lines, higher is the degree of interaction. However,

9 145 the interaction plot doesn't give information if the interaction is statistically significant. The interaction plot for ultimate tensile strength is given in Figure 6.4. Figure 6.4 Interaction plots for ultimate tensile strength ANALYSIS OF VARIANCE FOR UPSET The Anova table for upset is shown below Table 6.4. Source DF Seq SS Adj SS Adj MS F P RPM FRP FT FOP FOT Error Total Table 6.4 Analysis of variance for upset, using adjusted SS for tests The percentage contribution of factors for upset is given in pie chart format is shown in Figure 6.5.

10 146 Figure 6.5 Pie chart for percentage contribution for upset From the Figure 6.5 it is observed that friction time has highest contribution of 54% MAIN EFFECT PLOTS FOR UPSET The main effect plots for upset are given in Figure 6.6. Figure 6.6 Main effect plots for upset From the Figure 6.6, it is observed that upset is influenced by friction time, friction pressure, rpm and forging pressure. Friction

11 147 time has highest influence on upset. Upset increases as friction time, friction pressure and forging pressure increases. Higher friction time results in greater material consumption and lower productivity. Forging time has lease effect on upset INTERACTION PLOTS FOR UPSET The interaction plot for upset is given in Figure 6.7. Figure 6.7 Interaction plots for upset It is observed from the interaction plots that there is no interaction between friction time and forging pressure. It is also noticed that there is no interaction between forging time and friction time.

12 ANALYSIS OF VARIANCE FOR WELD INTERFACE TEMPERATURE Friction welding of SS 304 and Al 6061 has tendency of intermetallic layer formation at the weld interface. The growth and formation of intermetallic layer strongly depends on local temperature attained during welding. For good weld strength optimum amount of heat is required. The excessive heat generation during welding leads to intermetallic layer formation and it is responsible for brittle failure. The Anova for weld interface temperature is given below in Table 6.5. Source DF Seq SS Adj SS Adj MS F P RPM FRP FT FOP FOT Error Total Table 6.5 Analysis of Variance for weld interface temperature using Adjusted SS for Tests The percentage contribution of each weld parameter is shown in Figure 6.8. Friction time has maximum impact on weld interface temperature and it contributes 74 %. Forging pressure and forging time has least effect on weld interface temperature.

13 149 Figure 6.8 Pie chart for percentage contribution for weld interface temperature MAIN EFFECT PLOTS FOR WELD INTERFACE TEMPERATURE The main effect plot for weld interface temperature is given in Figure 6.9. Weld interface temperature increases as rpm, friction pressure and friction time increases. Friction time is very important parameter. Lower friction time results in irregular heating and its leads to incomplete weld. Higher friction time results in increase in formation of brittle intermetallic compounds and it also increases material consumption and decreases productivity. Friction pressure controls the temperature gradient in the weld zone. High rpm produces over heated structures whereas low rpm produces insufficient heat. Forging pressure has marginal effect on weld

14 150 interface temperature. The weld interface temperature decreases as forging pressure and forging time decreases. Figure 6.9 Main effect plots for weld interface temperature INTERACTION PLOTS FOR WELD INTERFACE TEMPERATURE The interaction plot for weld interface temperature is given in Figure Figure 6.10 Interaction plots for weld interface temperature

15 151 It is observed from the plot that there is no interaction between forging pressure and friction time and also between friction time and forging time. 6.4 REGRESSION ANALYSIS Regression analysis generates an equation to describe the statistical relationship between one or more predictors and the response variables to predict new observations. Regression generally uses the ordinary least squares method which derives the equation by minimizing the sum of the squared residuals. Regression analysis results indicate the direction, size, and statistical significance of the relationship between a predictor and response. Sign of each coefficient indicates the direction of the relationship. Coefficients represent the mean change in the response for one unit of change in the predictor while holding other predictors in the model constant. P-value for each coefficient tests the null hypothesis that the coefficient is equal to zero (no effect). Therefore, low p-values suggest the predictor is a meaningful addition to your model. The equation predicts new observations given by specified predictor values.

16 152 The regression coefficient of weld parameters vs responses (ultimate tensile strength, upset, weld interface temperature) are given in Table 6.6, Table 6.7 and Table REGRESSION ANALYSIS FOR ULTIMATE TENSILE STRENGTH The regression equation for ultimate tensile strength is UTS = RPM FRP FT FOP FOT (6.1) Where UTS ultimate tensile strength, RPM Rotation per minute, FRP Friction pressure, FT Friction time, FOP Forging pressure, FOT Forging time Predictor Coef SE Coef T P Constant RPM FRP FT FOP FOT Table 6.6 Regression coefficients of weld parameter vs ultimate tensile strength The scatter between estimated and observed values of ultimate tensile strength is shown in Figure Figure 6.11 Scatter between estimated and observed values of ultimate tensile strength

17 REGRESSION ANALYSIS FOR UPSET The regression equation is for upset is given below Upset = RPM FRP FT FOP FOT (6.2) Predictor Coef SE Coef T P Constant RPM FRP FT FOP FOT Table 6.7 Regression coefficients of weld parameter vs upset The scatter between estimated and observed values of upset is shown in Figure Figure 6.12 Scatter between estimated and observed values of upset REGRESSION ANALYSIS FOR WELD INTERFACE TEMPERATURE The regression equation for weld interface temperature is given below Temp = RPM FRP FT FOP FOT (6.3) Predictor Coef SE Coef T P Constant RPM FRP FT FOP FOT Table 6.8 Regression coefficients of weld parameter vs weld interface temperature

18 154 The scatter between estimated and observed values of weld interface temperature is shown in Figure Figure 6.13 Scatter between estimated and observed values of weld interface temperature 6.5 OPTIMIZATION The weld strength achieved during industrial experimentation is higher than parent material and upset and temperature are within limits and it is higher than weld strength of parent material, therefore further optimization is not considered. The optimized parameters were rpm 1400, friction pressure 70 MPa, friction time 1 second, forging pressure 140 MPa and forging time 6 second. The maximum tensile strength achieved was MPa and weld interface temperature achieved was 208 C. 6.6 CONTOUR PLOTS Contour plots helps to understand the impact of change in experimental factor on responses.

19 155 Figure 6.14 Contour plot for UTS vs friction pressure and forging pressure Figure 6.15 Contour plot for UTS vs RPM and forging pressure Figure 6.16 Contour plot for UTS vs RPM and friction pressure

20 156 Figure 6.17 Contour plot for UTS vs friction pressure and friction time Figure 6.18 Contour plot for upset vs friction pressure and friction time Figure 6.19 Contour plot for upset vs friction pressure and forging pressure

21 157 Figure 6.20 Contour plot for upset vs friction time and forging pressure Figure 6.21 Contour plot for temperature vs friction pressure and friction time Figure 6.22 Contour plot for temperature vs friction time and forging pressure

22 158 Figure 6.23 Contour plot for temperature vs RPM and friction pressure 6.7 EFFECT OF UPSET ON ULTIMATE TENSILE STRENGTH The effect of upset on ultimate tensile strength is shown in Figure The ultimate tensile strength increase as upset increases from 3 mm to 7.5 mm and beyond 7.5 mm upset leads to decrease in ultimate tensile strength. Figure 6.24 Effect of upset on tensile strength

23 EFFECT OF WELD INTERFACE TEMPERATURE ON ULTIMATE TENSILE STRENGTH The effect of weld interface temperature on ultimate tensile strength is shown in The ultimate tensile strength decrease as weld interface temperature increases. Figure 6.25 Effect of weld interface temperature on Tensile Strength 6.9 EFFECT OF UPSET ON WELD INTERFACE TEMPERATURE The effect of upset on weld interface temperature is shown in Figure Figure 6.26 Effect of upset on weld interface temperature

24 DATA ACQUISITION SYSTEM FOR ONLINE MONITORING OF WELDING PROCESS In this research, a data acquisition system (DAS) was developed to on-line monitor the quality of friction weld. The DAS captures the resultant variation of rpm, friction pressure, friction time, forging pressure, forging time, torque, axial displacement etc. The acceptance criteria for good weld strength for Al 6061 to SS 304 for online monitoring are given below: a) The weld interface temperature should be in the range of 175 C to 270 C. b) The upset should be in the range of 7 mm to 8.5 mm c) The application of upset pressure (0.6 second) before breaking and in rapid feed. (This was separately investigated in section of chapter 7) A fault alarm is set if the upset or temperature is out of range. The typical photo graph for good weld and bad weld is shown in Figure 6.27 and Figure 6.28 and a typical DAS graph for good weld and bad weld is shown Figure 6.29 and Figure 6.30.

25 161 Figure 6.27 Typical good quality weld Figure 6.28 Typical bad quality weld Figure 6.29 DAS graph for typical good weld Figure 6.30 DAS graph for typical bad weld