Optimization of the friction-stir-welding process and ... - SAGE Journals

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Optimization of the friction-stir-welding process and tool parameters to attain a maximum tensile strength of AA7075–T6 aluminium alloy S Rajakumar*, C Muralidharan, and V Balasubramanian Manufacturing Engineering, Annamalai University, Chidambaram, India The manuscript was received on 3 September 2009 and was accepted after revision for publication on 26 November 2009. DOI: 10.1243/09544054JEM1802

Abstract: High-strength, precipitation-hardening AA7075 alloy is used extensively in aircraft primary structures. The friction-stir-welding (FSW) process is an emerging solid-state joining process in which the material that is being welded does not melt and recast. The FSW process and tool parameters play a major role in deciding the joint strength. In this paper an attempt has been made to establish an empirical relationship between the FSW process and tool parameters (tool rotational speed, welding speed, axial force, shoulder diameter, pin diameter, and tool material hardness) and the tensile strength of the joint. Statistical tools such as design of experiments, analysis of variance, and regression analysis are used to develop the relationships. The developed empirical relationship can be effectively used to predict the tensile strength of FSW joints at the 95 per cent confidence level. A sensitivity analysis is also carried out and compared with the relative impact of input parameters on tensile strength in order to verify the measurement errors on the values of the uncertainty in estimated parameters. Keywords: friction stir welding, design of experiments, analysis of variance, response surface methodology, sensitivity analysis, optimization

1 INTRODUCTION High-strength, precipitation-hardening AA7075 aluminium alloy is used extensively in aircraft primary structures. However, this class of aluminium alloy is difficult to join by conventional fusion-welding techniques because the dendritic structure formed in the fusion zone can seriously deteriorate the mechanical properties of the joint. Recently, friction stir welding (FSW), a new joining process developed by the Welding Institute in Cambridge, UK [1], has emerged as a promising solid-state joining process with the potential to join aluminium alloys traditionally considered to be unweldable. Material subjected to FSW does not melt and recast, thus the resultant weldment offers advantages over conventional arc weldments, such as better retention of baseline mechanical properties, less distortion, lower *Corresponding author: Center for Materials Joining and Research (CEMAJOR), Manufacturing Engineering, Annamalai University, Annamalai Nagar, Chidambaram 608002, India. email: [email protected] JEM1802

residual stresses, and fewer weld defects [2]. To obtain the desired strength, it is essential to have complete control over the relevant process parameters to maximize the tensile strength on which the quality of a weldment is based. Therefore, it is very important to select and control the welding process parameters for obtaining the maximum strength. Various prediction methods can be applied to define the desired output variables through developing mathematical models to specify the relationship between the input parameters and output variables. The response surface methodology (RSM) is helpful in developing a suitable approximation for the true functional relationship between the independent variables and the response variable that may characterize the nature of the joints [3]. It has been proved by several researchers [4–7] that efficient use of statistical design of experimental techniques allows development of an empirical methodology, to incorporate a scientific approach in the fusionwelding procedure. In order to obtain high-quality welds in automated welding processes, selection of optimum parameters Proc. IMechE Vol. 224 Part B: J. Engineering Manufacture

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S Rajakumar, C Muralidharan, and V Balasubramanian

should be performed according to engineering facts. Generally, welding parameters are determined by trial and error, based on handbook values, and manufacturers’ recommendations. However, this selection may not yield optimal, or even in the vicinity of optimal, welding performance. Furthermore, it may cause additional energy and material consumption and may also result in low-quality welding. Besides, in industrial welding robots, even small changes in the welding process parameters may cause unexpected welding performance. Therefore, it is important to study stability of welding parameters to achieve high-quality welding. Prediction of the effects of small changes in design parameters provides very important information in engineering design. Therefore, by way of a mathematically modelled prediction system, the effects of any changes in the parameters on the overall design objective can be determined. This kind of analysis is known as design sensitivity analysis (DSA). Basically, sensitivity analysis (SA) yields information about the increment and decrement tendency of a design objective function with respect to design parameters [8]. There are few studies [9, 10] in which SA is performed using a mathematical model for different fusion-welding methods. The effects of FSW process parameters on tensile strength of aluminium alloys are well documented in the literature. Similarly, the influence of FSW tool parameters on tensile properties of aluminium alloys are also well reported in the literature. However, there is no literature available on the optimization of both the process and tool parameters of FSW of aluminium alloys; hence the present investigation was conducted.

2 SCHEME OF INVESTIGATION In order to achieve the desired objectives, the present investigation was planned as depicted in the flow chart provided in Fig. 1. 2.1 Identifying the important process parameters From the literature [11] and previous work conducted [11, 12] in the present authors’ laboratory, the predominant factors that have the greatest influence on tensile strength of the FSW process were identified. They are: (a) (b) (c) (d) (e) (f)

tool rotational speed; welding (traverse) speed; axial (downward) force; shoulder diameter; pin diameter; tool material hardness.

Proc. IMechE Vol. 224 Part B: J. Engineering Manufacture

These are the primary process and tool parameters contributing to the heat input and subsequently influencing the tensile properties of friction-stirwelded aluminium alloy joints. 2.2 Finding the working limits of the parameters The chemical composition of the base metal used in this investigation is presented in Tables 1 and 2. Large numbers of trial runs were carried out using 5 mm thick rolled plates of AA7075–T6 aluminium alloy to find out the feasible working limits of FSW process parameters. The working range of each process parameter was decided upon by inspecting the macrostructure (cross-section of the weld) for any visible defects such as tunnel defect, pinhole, kissing bond, lazy S, etc. Within the chosen working range of parameters, the joints were free from defects. Either below or above the range of parameters, the joints contained defects. Table 3 displays the macrographs to provide the evidence for fixing the feasible working range of welding parameters. The chosen level of important process parameters and tool parameters with their units and notations are presented in Table 4. 2.3 Conducting the experiments and recording the responses Rolled plates of 5 mm thickness, medium-strength aluminium AA7075–T6 alloy base metal, were cut to the required size (300 mm · 150 mm) by power hacksaw cutting and milling. Square butt joint configuration (300 mm · 300 mm) was prepared to fabricate the FSW joints. The initial joint configuration was obtained by securing the plates in position using mechanical clamps. The direction of welding was normal to the rolling direction. The joint dimensions are shown in Fig. 2(a). A single-pass welding procedure was followed to fabricate the joints. Nonconsumable tools made of high-carbon steel and high-speed steels were used to fabricate the joints. The tool nomenclature is shown in Fig. 2(b). Based on six factors, five level central composite designs, 15 tools were made with different tool pin diameter, shoulder diameter, and tool material hardness; the variations of tool dimensions are presented in Table 5. Five levels of tool material hardness were obtained by heat treating high-carbon steel in different quenching media (air, oil, water, furnace cooling). An indigenously designed and developed computer numerically controlled (CNC) FSW machine (22 kW; 4000 r/min; 6 ton, see Figs 2(c) to (d)) was used to fabricate the joints; as prescribed by the design matrix 52 joints were fabricated, as shown in Fig. 2(e). The welded joints were sliced using a power hacksaw and then machined to the required dimensions of tensile specimens, as shown in Fig. 2(f). JEM1802

Optimization of the friction-stir-welding process

1177

Statement of the problem and its objectives Identify the important input (process and tool) parameters

From literature and experience

Identify the important output parameters

Based on application

Based on literature, process knowledge. (Practical and theoretical understanding), trial runs, macro structure studies

Find upper and lower limits for input parameters

Considering no. of replicates, randomization of order of experiments, blocking or not etc., based on cost, time, accuracy of information, required and resources available

Developing the experimental design matrix

Conducting the experiment as per design matrix and recording the responses

Using FSW machine Selecting suitable statistical software package, using ANOVA, graphical methods, by conducting residual analysis and by checking the adequacy of the model By conducting further experiments and comparing actual and predicted values

Statistical analysis of data and development of a mathematical model by considering significant factors Validation of the developed model

No

Are the results satisfactory? Yes

Optimize the (process and tool) parameters Using RSM

To identify the sensitiveness of process and tool parameters on response

Sensitivity analysis Implementation of results Follow up

Fig. 1 Flow chart for scheme of investigation

Table 2 Table 1

Chemical composition (wt%) of the base metal

Element

Mg

Mn

Zn

Fe

Cu

Si

Cu

Al

Material

Base metal (7075–T6)

2.1

0.12

5.1

0.35

1.2

0.58

1.2

Bal

Base metal (7075–T6)

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Mechanical properties of the base metal Yield strength (MPa)

Ultimate tensile strength (MPa)

Elongation (%)

Vicker hardness (HV 0.05)

410

485

12

160


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Table 3 Input parameters

Parameter range

Macrostructure observation of AA7075–T6 aluminium alloy

Macrostructure

Name of defect

Probable reason

Rotational speed

< 900 r/min

Worm hole

Insufficient heat generation and insufficient metal transportation

Rotational speed

> 1900 r/min

Pin hole

Increase in turbulence of the plasticized metal

Welding speed

< 12 mm/min

Tunnel defect

Excess heat input per unit length of the weld and no vertical movement of the metal

Welding speed

> 125 mm/min

Kissing bond

Increase in welding speed resulted in poor plasticization of metal and associated defect

Axial force

< 5 kN

Tunnel defect

Insufficient axial force and inadequate heat generation

Axial force

> 11 kN

Worm hole

Additional axial force leads to excess heat input and thinning of the weld zone

Shoulder diameter

< 7 mm

Tunnel defect

Insufficient stirring, butt surfaces could be directly bonded without the metallic bond between oxide-free surfaces in the root part of the weld

Shoulder diameter

> 22 mm

Tunnel hole

Excessive heat input due to softening and workhardening effect

Pin diameter

< 2 mm

Piping defect

Asymptote heat generation and insufficient metal transportation

Pin diameter

> 7 mm

Tunnel defect

Excessive heat input due to softening

Tool material hardness

< 200 HV

Pin hole

Results from low frictional heat generation.

> 900 HV

Piping defect

High frictional heat generation.

Tool material hardness

The specimens were prepared as per the ASTM E8M-04 guidelines. A tensile test was carried out in a 100 kN, servo-controlled universal testing machine (made by FIE-BLUESTAR, India). The specimen was Proc. IMechE Vol. 224 Part B: J. Engineering Manufacture

loaded at the rate of 1.5 kN/min as per ASTM specifications, so that the tensile specimens undergo uniform deformation. The specimen finally failed after necking and the load versus displacement was JEM1802


Table 4

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Important FSW process parameters and their levels for AA7075 aluminium alloy Factor levels

Factors

Notation

2.378

1

0

þ1

þ 2.378

Tool rotational speed (r/min) Welding speed (mm/min) Axial force (kN) Tool shoulder diameter (mm) Pin diameter (mm) Tool hardness (HV)

N S F D d H

924 12.43 5.62 7.86 2.62 243

1200 40 7 12 4 450

1400 60 8 15 5 600

1600 80 9 18 6 750

1875 107.56 10.37 22.13 7.37 956

(a) Joint dimensions (in ‘mm’)

(b) Nomenclature of FSW tool

(c) FSW machine

(d) Close-up view

(f) Dimensions of flat tensile specimens (in ‘mm’) (e) Fabricated joints

Fig. 2 Experimental details JEM1802


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Table 5


Details of tool dimensions (pin length ¼ 4.7 mm)

Tool dimensional variations

Shoulder diameter, D (mm)

Pin diameter, d (mm)

Tool material hardness (HV)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

12 12 18 18 12 12 18 18 15 09 21 15 15 15 15

4 4 4 4 6 6 6 6 5 5 5 3 7 5 5

450 750 450 750 450 750 450 750 600 600 600 600 600 300 900

(D), pin diameter (d), and tool material hardness (H); it can be expressed as TS ¼ f ðN ; S; F; D; d; H Þ The second-order polynomial (regression) equation used to represent the response surface Y is given by [14] X X X bi x i þ bii xi2 þ bij xi xj þ er ð2Þ Y ¼ b0 þ and for six factors, the selected polynomial could be expressed as TS ¼ b0 þ b1 ðN Þ þ b2 ðSÞ þ b3 ðF Þ þ b4 ðDÞ þ b5 ðd Þ þ b6 ðH Þ þ b11 N 2 þ b22 S2 þ b33 F 2 þ b44 D2 þ b55 d 2 þ b66 H 2

recorded. At each experimental condition three specimens were tested and the average of three results is presented in Table 6.

þ b12 ðNSÞ þ b13 ðNF Þ þ b14 ðNDÞ þ b15 ðNd Þ þ b16 ðNH Þ þ b23 ðSF Þ þ b24 ðSDÞ þ b25 ðSdÞ þ b26 ðSH Þ þ b34 ðFDÞ þ b35 ðFd Þ þ b36 ðFH Þ þ b45 ðDd Þ þ b46 ðDH Þ þ b56 ðDH Þ ð3Þ

3 DEVELOPING AN EMPIRICAL RELATIONSHIP 3.1 Response surface methodology Response surface methodology is a collection of mathematical and statistical techniques useful for analysing problems in which several independent variables influence a dependent variable or response and the goal is to optimize the response [13]. In many experimental conditions, it is possible to represent independent factors in quantitative form as given in equation (1). Then these factors can be regarded as having a functional relationship or response as follows Y ¼ Fðx1 ; x2 ; :::; xk Þ er

ð1Þ

Between the response Y and x1, x2, . . . , xk of k quantitative factors, the function F is called the response surface or response function. The residual er measures the experimental errors. For a given set of independent variables, a characteristic surface is responded. When the mathematical form of F is not known, it can be approximated satisfactorily within the experimental region by a polynomial. In the present investigation, RSM has been applied for developing the mathematical model in the form of multiple regression equations for the quality characteristic of the friction-stir-welded AA7075 aluminium alloy. In applying the RSM, the independent variable was viewed as a surface to which a mathematical model is fitted. Representing the tensile strength of the joint by TS, the response is a function of rotational speed (N), welding speed (S), axial force (F ), shoulder diameter Proc. IMechE Vol. 224 Part B: J. Engineering Manufacture

In order to estimate the regression coefficients, a number of experimental design techniques are available. In this work, central composite face-centred (CCF) design (Table 6) was used, which fits the second-order response surfaces very accurately. A CCF design matrix with the star points at the centre of each face of factorial space was used, so a ¼ – 2.378. This variety requires three levels of each factor. CCF designs provide relatively high-quality predictions over the entire design space and do not require the use of points outside the original factor range. The upper limit of a factor was coded as þ 2.378, and the lower limit was coded as 2.378. All the coefficients were obtained applying CCF design using the Design Expert statistical software package. After determining the significant coefficients (at the 95 per cent confidence level), the final model was developed using only these coefficients and the final mathematical model to estimate tensile strength is given as follows. Tensile strength of the FSW joint of a AA7075 alloy is TS ¼ f372:9 þ 2:84ðN Þ þ 4:47ðSÞ þ 3:35ðF Þ þ 2:79ðDÞ þ 2:40ðd Þ þ 1:29ðH Þ þ 2ðNSÞ 1:06ðNF Þ 1:37ðNDÞ 0:12ðNd Þ 2:87ðNH Þ 0:125ðSF Þ 1:062ðSDÞ 1:812ðSd Þ 1:062ðSH Þ 1:25ðFDÞ 1:75ðFd Þ 0:75ðFH Þ þ 0:687ðDdÞ 0:812ðDH Þ 1:812ðdH Þ 8:039 N 2 5:74 S2 4:41 F 2 5:12 D2 4:76 d 2 5:47 H 2 g MPa ð4Þ JEM1802


Table 6

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Experimental design matrix and results (AS ¼ advancing side; RS ¼ retreating side; WN ¼ weld nugget) Input parameter

Expt no.

Tool rotational speed (r/min)

Welding speed (mm/min)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2.378 2.378 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 2.378 2.378 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Axial force (kN)

Tool shoulder diameter (mm)

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 2.378 2.378 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 2.378 2.378 0 0 0 0 0 0 0 0 0 0 0 0

The adequacy of the developed model was tested using the analysis of variance (ANOVA) technique and the results of second-order response surface model fitting in the form of ANOVA are given in Table 7. The determination coefficient (R2) indicates the goodness of fit for the model. In this case, the value of the determination coefficient (R2 ¼ 0.9673) JEM1802

Output response

Pin diameter (mm)


Tensile strength of welded joints (MPa)

Occurrence of defect region

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 2.378 2.378 0 0 0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 2.378 2.378 0 0 0 0 0 0 0 0

302 327 332 346 342 332 339 355 337 331 333 345 334 336 346 353 334 339 330 344 333 335 340 354 338 341 346 354 345 350 346 351 325 328 332 347 340 354 339 347 342 348 338 344 376 375 374 367 371 373 373 375

AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS

indicates that 96.73 per cent of the total variability is explained by the model after considering the significant factors. The models are not over fitted as indicated by the comparison of R2 and R2-adjusted values. Only less than 3 per cent of the total variations are not explained by the model. The value of adjusted determination coefficient (adjusted R2 ¼ 0.9305) is Proc. IMechE Vol. 224 Part B: J. Engineering Manufacture

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Table 7


ANOVA test result (df is degrees of freedom; CV is coefficient of variation; F is Fisher’s ratio; p is probability)

Source

Sum of squares

df

Mean square

F-value

p-value (prob. > F)

Model* N* S* F* D* d* H* NS* NF ND Nd NH* SF SD Sd* SH FD Fd* FH Dd DH dH* N 2* S2* F2* D 2* d 2* H2* Residual Lack of fit Pure error Cor. total Std deviation Mean CV (%) PRESS

11 294.48 350.0575 866.0186 487.4081 338.1749 251.0137 73.103 13 128 36.125 60.5 0.5 264.5 0.5 36.125 105.125 36.125 50 98 18 15.125 21.125 105.125 3749.671 1912.365 1131.122 1522.403 1319.511 1739.8 381.4452 323.4452 58 11 675.92 3.986 672 344.9615 1.155 686 2218.408

27 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 24 17 7 51

418.314 350.0575 866.0186 487.4081 338.1749 251.0137 73.103 13 128 36.125 60.5 0.5 264.5 0.5 36.125 105.125 36.125 50 98 18 15.125 21.125 105.125 3749.671 1912.365 1131.122 1522.403 1319.511 1739.8 15.893 55 19.026 19 8.285 714

26.319 73 22.025 13 54.488 67 30.667 03 21.277 49 15.793 43 4.599 546 8.053 581 2.272 934 3.806 575 0.031 459 16.641 97 0.031 459 2.272 934 6.614 318 2.272 934 3.145 93 6.166 023 1.132 535 0.951 644 1.329 155 6.614 318 235.924 120.3233 71.168 59 95.787 49 83.021 77 109.4658

< 0.0001 < 0.0001 < 0.0001 < 0.0001 0.0001 0.0006 0.0423 0.0091 0.1447 0.0628 0.8607 0.0004 0.8607 0.1447 0.0167 0.1447 0.0888 0.0204 0.2978 0.3390 0.2603 0.0167 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001

2.296 264

0.1336

R2 Adj. R2 Pred. R2 Adeq. precision

Significant

Not significant

0.967 331 0.930 578 0.810 002 21.8504

* Significant factor.

also high, which indicates a high significance of the model. Predicted R2 ¼ 0.8100 is in good agreement with the adjusted R2 and shows that the model would be expected to explain 81 per cent of the variability in new data. Adequate precision was found to be 21.85, which indicates that the model will give reasonable performance in prediction. A ratio > 4 is desirable. At the same time a relatively lower value of the coefficient of variation (CV ¼ 1.15) indicated a high degree of precision and a good deal of reliability of the conducted experiments (experimental values). ‘PRESS’ is a measure of how well the model of the experiment is likely to predict the responses in a new experiment. Small values of PRESS are desirable. The model F-value of 26.31 implied that the model was significant and a ‘model F-value’ this large would occur as a result of noise. A p-value less than 0.05 indicated the significant model terms. Value of probability > F in Table 7 for the model is less than 0.05, which indicates that the model is significant. Lack of fit is insignificant and therefore indicates that the model fits well with the experimental data. The Proc. IMechE Vol. 224 Part B: J. Engineering Manufacture

high p-value for the lack-of-fit test also indicates that the model does adequately fit with the response surface for tensile strength. The normal probability plot of the residuals for tensile strength shown in Fig. 3 reveals that the residuals are falling on a straight line, which means the errors are distributed normally [14]. All the above considerations indicate an excellent adequacy of the regression model. Each observed value is compared with the predicted value calculated from the model in Fig. 4.

4 OPTIMIZING THE PARAMETERS Response surfaces were developed for the model, taking two parameters in the middle level and two parameters in the X and Y axis and response in the Z axis. The response surfaces clearly reveal the optimal response point. RSM is used to find the optimal set of process parameters that produce a maximum or minimum value of the response [15]. In the present investigation the process parameters corresponding JEM1802


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Fig. 3 Normal probability plot of residuals for tensile strength

Fig. 4 Normal probability plot of experimental versus predicted tensile strength

to the maximum tensile strength are considered as optimum (analysing the contour graphs and by solving equation (4)). Hence, when these optimized process parameters are used, it will be possible to attain the maximum tensile strength. Figure 5 presents three-dimensional response surface plots for the response tensile strength obtained from the JEM1802

regression model. The optimum tensile strength is exhibited by the apex of the response surface. Contour plots play a very important role in the study of the response surface analysis. By generating contour plots using software for response surface analysis, the optimum is located with reasonable accuracy by characterizing the shape of the surface. If Proc. IMechE Vol. 224 Part B: J. Engineering Manufacture

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Fig. 5 Response graphs

a contour patterning of circular-shaped contours occurs, it tends to suggest independence of factor effects, whereas elliptical contours may indicate factor interactions [16–18]. Figure 6(a) exhibits an almost circular contour, which suggests independence of factor. It is relatively easy to see, by examining the contour plots in Figs 6 (b) to (e), that changes in the tensile strength are more sensitive to changes in rotational speed than to changes in welding speed, axial force, shoulder diameter, pin diameter, or tool material hardness. When rotational speed is compared with welding speed at a constant tool material hardness of 600 HV, shoulder Proc. IMechE Vol. 224 Part B: J. Engineering Manufacture

diameter of 15 mm, pin diameter of 5 mm, and axial force of 8 kN, rotational speed is slightly more sensitive to changes in tensile strength, as illustrated in contour plot Fig. 6(b). The interaction effect between rotational speed and welding speed is more significant than the interaction effect between other combinations of parameters. Maximum tensile strength estimated from the response surface and contour plots is 374.95 MPa, which is given by the following optimized FSW process and tool parameters: rotational speed of 1438 r/min, welding speed of 67.64 mm/min, axial force of 8.29 kN, shoulder diameter of 15.54 mm, pin diameter of JEM1802


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Fig. 6 Contour graphs

5.13 mm, and tool material hardness of 600 HV. The above values were also verified using statistical software Minitab. The corresponding optimization plot is depicted in Fig. 7. Three joints were fabricated using the optimum parameters (listed above) and average tensile strength of a FSW AA7075–T6 aluminium alloy joint was found to be 377 MPa, which shows excellent agreement with the predicted values. Microstructure of the weld region of the FSW joint fabricated using optimum parameters is shown in Fig. 8(a). This reveals that there is no micron level defect as a result of sufficient heat generation and adequate plastic JEM1802

flow of the material. Moreover, the grains are found to be finer than the base metal grains (Fig. 8(b)). The average grain diameter size was measured in the stir zone and found to be smaller (30 mm) than base metal grain (85 mm). The fracture surfaces of the tensile tested specimens were characterized using scanning electron microscopy to understand the mode of the failure. All the fracture surfaces invariably consist of dimples, which is an indication that most of the failure is the result of ductile fracture. The dimples on the fracture surface of the base metal (Fig. 9(a)) are larger than the dimples on the fracture surface of the stir zone (Fig. 9(b)). Proc. IMechE Vol. 224 Part B: J. Engineering Manufacture

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Fig. 7 Optimization plots

b

a

100µm

100µm

(a) Stir zone

(b) Base metal

Fig. 8 Optical micrographs of base metal and FSW joints

a

b

(a) Base metal

(b) Stir zone

Fig. 9 Scanning electron microscopy fractographs of the top surface of tensile specimens

5 SENSITIVITY ANALYSIS Sensitivity analysis, a method to identify critical parameters and rank them by their order of importance, is paramount in model validation where attempts are made to compare the calculated output with the measured data. This type of analysis can be Proc. IMechE Vol. 224 Part B: J. Engineering Manufacture

used to study which parameters must be most accurately measured, thus determining the input parameters exerting the most influence on model response [19]. Therefore, SA plays an important role in determining which parameter of the process should be modified in order to obtain the improved response characteristics. Mathematically, sensitivity JEM1802


of an objective function with respect to a design variable is the partial derivative of that function with respect to its variables [20]. In the present investigation the aim is to predict the tendency of tensile strength owing to a small change in process parameters for the FSW process. The sensitivity equations are obtained by differentiating the developed empirical relation with respect to the factors of interest such as rotational speed, welding speed, axial force, shoulder diameter, pin diameter, and tool material hardness, which are explored here. The sensitivity equations (5) to (10) represent the sensitivity of tensile strength for rotational speed, welding speed, axial force, shoulder diameter, pin diameter, and tool material hardness respectively. @TS=@N ¼ 2:84 þ 2S 1:062F 1:375D 0:125d 2:875H 16:07N

ð5Þ

@TS=@S ¼ 4:47 þ 2N 0:125F 1:062D 1:812d 1:062H 11:482S

ð6Þ

@TS=@F ¼ 3:354 1:062N 0:125S 1:25D 1:75d 0:75H 8:830F

Table 8

ð7Þ

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@TS=@D ¼ 2:79 1:37N 1:062S 1:25F þ 0:687d 0:812H 10:24D

ð8Þ

@TS=@d ¼ 2:40 0:125N 1:812S 1:75F þ 0:687D 1:812H 9:53d ð9Þ @TS=@H ¼ 1:299 2:875N 1:062S 0:75F 0:812D 1:812d 10:95H

ð10Þ

Sensitivity information should be interpreted using the mathematical definition of derivatives, namely, positive sensitivity values imply an increment in the objective function by a small change in design parameter, whereas negative values state the opposite [21]. Sensitivities of process parameters on tensile strength are presented in Table 8. Figure 10 shows the sensitivity of rotational speed, welding speed, axial force, shoulder diameter, pin diameter, and tool material hardness respectively on tensile strength. The small variation of rotational speed causes large changes in tensile strength when the hardness increases. The results reveal that the tensile strength is more sensitive to rotational speed than axial force, shoulder diameter, pin diameter, tool material hardness, or welding speed.

Tensile strength sensitivities of (process and tool) parameters (S ¼ 80 mm/min) Sensitivity

Rotational speed (r/min)

Shoulder diameter (mm)

Pin diameter (mm)


Tensile strength (MPa)

@TS/@N

6

1000 1200 1400 1600 1800 1000 1200

9 12 15 18 21 9 12

3 4 5 6 7 3 4

300 450 600 750 900 300 450

194.6 300.8 347.5 334.8 262.7 220.7 322

47.8 27.4 6.9 13.4 33.9 46.8 26.3

7

1400 1600 1800 1000 1200

15 18 21 9 12

5 6 7 3 4

600 750 900 300 450

363.9 325.4 269.5 238 334.5

8

1400 1600 1800 1000 1200

15 18 21 9 12

5 6 7 3 4

600 750 900 300 450

9

1400 1600 1800 1000 1200

15 18 21 9 12

5 6 7 3 4

10

1400 1600 1800

15 18 21

5 6 7

Axial force (kN)

JEM1802

@TS/@F

@TS/@D

@TS/@d

@TS/@H

@TS/@S

30.5 25.7 20.8 16.0 11.2 21.6 16.8

27.7 15.9 4.2 7.5 19.2 26.4 14.7

25.6 14.8 4.0 6.7 17.4 23.9 13.1

34.6 18.1 1.7 14.7 31.1 33.8 17.4

2.8 4.8 6.7 8.6 10.6 3.0 4.9

5.9 14.5 35.0 45.7 25.2

12.0 7.2 2.4 12.8 8.0

2.9 8.7 20.5 25.2 13.4

2.3 8.4 19.2 22.1 11.3

0.9 15.4 31.9 33.1 16.6

6.8 8.8 10.7 3.1 5.0

371.6 349.3 267.5 264.4 338.1

4.8 15.6 36.0 44.6 24.2

3.2 1.5 6.3 4.0 0.7

1.7 10.0 21.7 23.9 12.2

0.58 10.2 20.9 20.4 9.6

0.2 16.2 32.6 32.3 15.9

7.0 8.9 10.8 3.2 5.2

600 750 900 300 450

370.4 343.3 256.7 246.1 332.9

3.7 16.6 37.1 43.6 23.1

5.6 10.4 15.2 4.8 9.6

0.4 11.2 23.0 22.7 10.9

1.1 11.9 22.7 18.6 7.8

0.5 16.9 33.4 31.6 15.1

7.1 9.0 11.0 3.3 5.3

600 750 900

360.4 328.4 237.1

2.7 17.7 38.1

14.4 19.2 24.0

0.7 12.5 24.2

2.9 13.7 24.4

1.2 17.7 34.1

7.2 9.1 11.1


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Fig. 10 Sensitivity analysis results: (a) rotational speed (r/min); (b) welding speed (mm/min); (c) axial force (kN); (d) shoulder diameter (mm); (e) pin diameter (mm); (f) tool material hardness (HV)

6 DISCUSSION In FSW, the tool rotation speed factor is more sensitive than other parameters and this is revealed by SA. In particular, heat generation due to friction is mainly dependent on tool rotational speed. The welding speed decides only the quantity of heat supplied to the base materials to be joined. If the heat generation is less, then heat supplied will be relatively less, and vice versa. A lower tool rotational speed produces less heat generation, irrespective of welding speed, subsequently heat supplied to the base material is less, which causes insufficient material flow and less plasticization in the stir zone – and hence the tensile strength is lower. A higher rotational speed produces high heat generation, irrespective of welding speed, Proc. IMechE Vol. 224 Part B: J. Engineering Manufacture

and consequently heat supplied to the base material is high, which causes turbulent material flow and grain coarsening in the stir zone – and hence the tensile strength is lower. Neither low heat input nor high heat input is preferred in FSW, owing to the lower tensile strength resulting and this can be clearly seen from Fig. 11(a). In FSW, the tool material hardness will decide the coefficient of friction m. If m is higher, then friction between tool and base metal will be greater and the resultant heat generation will be higher. If m is lower, then friction will be less and the resultant heat generation will be lower. Hence, the coefficient of friction (in other words, the tool material hardness) will control the heat generation at all tool rotational speeds. The lower tool material hardness and lower JEM1802


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Fig. 11 Two-factor interaction effect of significant factors: (a) rotational speed and welding speed; (b) rotational speed and tool material hardness; (c) welding speed and pin diameter; (d) axial force and pin diameter; (e) pin diameter and tool material hardness

tool rotational speed combined will produce less heat generation, and consequently heat supplied to the base material will be less, which will deteriorate tensile strength of the joint as explained above. On the other hand, higher tool material hardness and higher tool rotation speed combined will produce higher heat generation; consequently heat supplied to the base material will be higher, which again will deteriorate tensile strength of the joint as explained above. This is clearly evident from Fig. 11(b). In FSW, the pin diameter decides the volume of material that is being plasticized/stirred. If the pin diameter is larger, then the volume of material stirred will be higher, and vice versa. The smaller pin diaJEM1802

meter and lower welding speed combine to cause higher heat supplied to a smaller volume of material. This will lead to turbulent material flow and grain coarsening in the weld region. On the other hand, the higher pin diameter and higher welding speed combine to cause lower heat supplied to a larger volume of material. This will lead to insufficient material flow and inadequate plasticization. Both these conditions lead to lower tensile strength in FSW joints and this is evident from in Fig. 11(c). Axial force is another important parameter that will influence the heat generation as well as material flow behaviour under the rotating shoulder pin. If axial force is lower, then the friction between the tool Proc. IMechE Vol. 224 Part B: J. Engineering Manufacture

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shoulder and the base material will be lower and this will lead to a low-heat-input condition, and vice versa. The lower axial force and the smaller pin diameter combine to cause a low-heat-input condition. This will cause inadequate plasticization and insufficient material flow in the weld region. On the other hand, higher axial force and smaller pin diameter combine to cause a high-heat-input condition. This will lead to turbulent material flow and grain coarsening in the weld region. Both of these conditions lead to lower tensile strength in FSW joints and this is evident from Fig. 11(d). The effects of pin diameter and tool material hardness have been explained in detail earlier, and these can be clearly seen in Fig. 11(e).

7 CONCLUSIONS The following important conclusions are derived from this investigation. 1. An empirical relationship was developed to predict the tensile strength of friction-stir-welded AA7075–T6 aluminium alloy joints at the 95 per cent confidence level, incorporating FSW process and tool parameters. 2. A maximum tensile strength of 375 MPa was exhibited by the FSW joints fabricated with the optimized parameters of 1438 r/min rotational speed, 67.64 mm/min welding speed, 8.29 kN axial force, shoulder diameter of 15.54 mm, pin diameter of 5.13 mm, and tool material hardness of 600 HV. 3. Rotational speed was more sensitive than the other parameters, followed by welding speed, axial force, tool material hardness, pin diameter, and shoulder diameter.

ACKNOWLEDGEMENTS The authors are grateful to the Department of Manufacturing Engineering, Annamalai University, Annamalai Nagar, India for extending the facilities of the Material Testing Laboratory to carry out this investigation. The authors wish to record their sincere thanks to the Clean Technology Division of the Ministry of Environment and Forest, Government of India, New Delhi for financial support rendered through R&D Project No. MoEF1-9/2005-CT. The authors would also like to thank E. Balamurugan, post graduate student, K. Subramaniyan, senior foreman – manufacturing engineering, S Muthukumaran, N. Sairaman, and A. K. Thillaiparaman, project assistants, CEMAJOR for their help and support. In addition, the authors would like express their graProc. IMechE Vol. 224 Part B: J. Engineering Manufacture

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