OPTIMIZATION BY RESPONSE SURFACE METHODOLOGY BASED ON DESIRABILITY FUNCTION APPROACH OF FRICTION STIR WELDING
Ravi Kushwaha
Department of Mechanical Engineering, BM College of Technology, RGPV, Bhopal Professor Purushottam Kumar Sahu
Department of Mechanical Engineering, BM College of Technology, RGPV, Bhopal Abstract - Friction stir welding (FSW) is comparatively new solid state connection method.
This connection technique is energy economical, atmosphere friendly and versatile. Welding could be a multiinput-output method within which quality of welded joint is depends upon an input parameter. Thus optimization of input method parameter is needed to realize smart quality of welding. There square measure such a lot of strategies of optimization within which Taguchi technique and Response surface methodology square measure designated for optimization of method parameter. Optimum combination of method parameters setting is found: tool rotation speed of one thousand rates, tool travel speed of twenty mm/min & two No. of passes for achieving a weld joint having superior maximum hardness (HRB), viz., with a 95% confidence level, is developed using response surface methodology to predict the hardness of the weld joint. ANOVA technique is used to determine the adequacy of the developed model and identify the significant terms. The desirability function is used to analyze the responses and predict the optimal process parameters. 6. An optimum hardness, Tool Rotation Speed (rpm) A2 1000 rpm (b) Tool Travel Speed (mm/min), B1 20 mm/min (c) No. of passes the error between predicted and confirmatory test is obtained approximately 1%, which validates the used optimization technique.
Index Terms: Response surface methodology, Taguchi method, Hardness, desirability function.
1. INTRODUCTION
As the FSW process is carried out at lower temperatures, it doesn‟t experience much distortion and residual stresses. This technique also imparts great dimensional stability. Unlike the conventional welding processes, FSW doesn‟t require any filler material or shielding gas. Due to innumerable such qualities and advantages of FSW over conventional joining processes, it has become a rapidly emerging solid-state joining method [3].Process parameters such as „„rotational speed, welding traverse speed, Tilt angle, axial force, probe and shoulder profile”, etc. must be optimized for successful implementation of FSW. The FSW process is represented in Fig. 1 (Reproduced from Ref. [5], with permission from Elsevier).
Fig.1.1 Illustrative representation of FSW [4] (Reproduced from Ref. [4], with
permission from Elsevier).
1.1 Principle of FSW Technique
Friction Stir Welding is a solid state welding process in which a cylindrical shape shouldered tool rotates on the surface of the workpiece to be joined. The workpiece is which firmly clamped in the fixture of FSW machine and during the rotation of the tool, the axial force is applied on the welding surface. A non-consumable tool is used to join workpieces without melting it during the welding process and the workpiece is plasticized due to the generation of heat and friction.
The basic concept behind FSW is simple: A non-consumable rotating tool with a specially designed pin and shoulder is inserted into the abutting edges of the two parts to be joined and traversed along the line of joint.
In friction stir welding process a cylindrical shouldered tool with a profiled probe is rotated and gradually plunged between two pieces of sheet or plate material to be welded together to form a joint as shown in Fig.1. The pieces must be clamped onto a back bar in such a way that the abutting joint faces are not pulled apart or pushed out of place in any other way.
The wear-resistant welding tool and workpiece material produce frictional heat.
The heat causes the workpieces to be softened after having reached the required temperature which allows the machine to cross the weld line. The resulting plasticized material is moved from top to the trailing edge of the tool and is bonded together by the intimate contact between the shoulder of the tool and the head of the pin, creating a solid-phase connection between the two parts. The process can be considered as a technique of solid-phase keyhole welding since a hole is generated to accommodate the probe and then moved through the welding sequence. The method was initially limited to low-melting-temperature materials as initial tool materials could not hold up to the tension of higher- temperature „„stirring” materials such as steels and other high-strength materials.
Recently this problem has been solved by introducing new tool material technologies such as polycrystalline cubic boron nitride (PCBN), tungsten rhenium, and ceramics.
The use of a liquid cooled tool holder and telemetry device has further improved method and capability.
Fig. 1.2 Schematic of the FSW process In the FSW process a non-consumable rotating tool with a specially designed pin and shoulder is inserted into the abutting edges of workpieces to be joined and traversed along the line of the joint, as shown in Fig. 1 [5]. As the tool travels, heat is created by the contact friction between the shoulder and the workpiece, and by the plastic deformation of the materials in the stir zone. The high strain and heat energies experienced by the base metal during stirring causes dynamic recrystallization, which is the formation of new grains in the weld zone [6]. Although Fig. 1 shows a butt joint for illustration, other types of joints, as shown in Fig. 2, also can be fabricated by FSW [6].
Fig. 1.3 Schematic drawing of FSW process [5].
1.2 FSW Process
The properties and performance of the FSW joints are dictated by the microstructure, which in turn is determined by the FSW process. The FSW process can normally be varied by changing the welding parameters.
Therefore welding parameters must be selected that give the best possible microstructure. Furthermore an accurate knowledge of the FSW process is a prerequisite for reliable prediction of the weld dimensions, final microstructure and mechanical properties of the FSW joints.
Due to the complexity of the FSW process, it is very difficult to gain insight into the joint during the actual forming process.
Numerical simulation helps overcome this problem by providing an effective way of analyzing the formation of FSW joints [7].
2 LITERATURE REVIEW
On the basis of literature little researcher work on validation of experimental work from simulation software the use of FSW has now been extended to other alloys such as magnesium, copper, steel, brass and composites. The process should be simulated with some advanced methods like MAT lab, ANN, Taguchi, GRE.RSM.PSO etc.
S. Jambulingam [12] selected material were AA7075 and AA3014 and joined by friction stir welding, 9 experiments were conducted at different speed, feed, and axial force. Taguchi method was used for design of experiments. He found optimum parameters were 1200 rpm speed, 10 mm/min feed with cylindrical tool profile.
Shaikh and Chouhan [20] used material were AA6061 T6 and AA2024 T0.
For design of experiment Taguchi method based on L9 orthogonal array were used.
Hardness measure in Vickers hardness tester and tensile stress and yield stress are
measured. Optimization analysis was done using MINITAB. Dissimilar metal joining process using friction stir welding is difficult to achieve because of different co- efficient of heat and the base metal chemical composition. Welding parameters like rotational speed, traverse speed, axial force and tilt angle which plays a very important role for increasing the weld quality.
Pradeep and Muthukumaran [21] used tool having a conical pin of 0.4 mm clearance. Process parameters were optimized by using the Taguchi technique.
Experiments designed based on three process parameters, tool rotational speed, tool tilt angle and travel speed. Tensile strength was used as response. A conical shorter pin tool was better strength of weld at a lower travel speed, and it helps in the reduction of tool wear, by lesser usage of the tool material.
Jawdat A. and Al-Jarrah [22] Worked for optimization of FSW Parameters for Joining Aluminum Alloys Using RSM. He used aluminum alloy sheets with thicknesses of 4, 5, 6, 7and 8 mm were Butt jointed. High carbon steel used as tool material with flat cylindrical shoulder diameters of 18, 21, 24, 27 and 30 mm.
rotational speed range used were 400, 700, 1000, 13000 and 1600 rpm. And welding speed range used 0.5, 1.0, 1.5, 2.0 and 2.5 mm/sec. it was found that a general result for a plate thickness of 6 mm, the best combination to have maximum yield strength is 1000 rpm rotational speed with 1.5 mm/sec welding speed and a shoulder diameter of 24 mm. This experiment results defect free joints though, the superiority of welded joints depends on controlling the rotation speed with welding speed to fill up the cavity behind the pin when moving forward.
3 METHODOLOGY 3.1 Objective of Work
In summary, the review of response surface methodology in Friction Stir Welding to focusing on 60/40 brass plates has been successfully conducted. This will provide a comprehensive insight for the current and also provide the current state of research on response surface methodology in friction stir welding to 60/40 brass plates in order to fill the gaps with new research approaches and ideas. Furthermore, new studies on response surface methodology in Friction Stir Welding to 60/40 brass plates
with respect to the selection of cost effective FSW tools and process optimization to produce sound welds still needs to be developed.
Response surface methodology (RSM) and regression analysis (RA) for predicting the hardness (HRB) strength, percentage of elongation and hardness of brass which is widely used in automotive, aircraft and defense industries by incorporating friction stir welding(FSW) process parameter such as tool rotation speed of 1000 rpm, tool travel speed of 20 mm/min & 2 No. of passes. Established mathematical quadratic regression model was very much fit to the actual experimental results. This experimental analysis also determines the elite combination of input parameters of the FSW technique, which would exhibit the best values of above mentioned output parameters.
Table 3.1 Chemical Composition of 60/40 Brass [5]
Element Cu Zn Ni Sn Fe
Amount 60% 40% 0.3% 0.3% 0.05%
Table 3.2 Mechanical properties of 60/40 Brass [5]
Tensile Strength
(MPa)
Yield Strength
(MPa)
Elongat ion (%)
Hardness (HRB)*
372-510 145-379 10-52 63 Microstructural analysis was carried out on cross section of 60/40 brass specimen Processed at optimum parameter setting (derived from Taguchi analysis) and that of base metal using optical microscopy.
Standard metallographic procedure was followed including sectioning, grinding, intermediate polishing with SiC paper of coarse to fine grit and fine polishing with alumina slurry. Etching was carried out using mixture of Nitric acid HNO3 (50%) and Distilled water (50%). The samples were immersed in the etchants for 90 seconds.
Micrographs were taken using a Metzer make optical microscope with camera attachments at 200X magnification.
Fig.3.1 cause and effect diagram for hardness influencing parameters Table 3.3 Significant parameters of FSW
technique and their levels for Brass
Level A B C
Tool Rotation Speed (rpm)
Tool Travel Speed (mm/min)
No. of passe
s
1 710 20 1
2 1000 28 2
3 1400 40 3
Table: 3.4 Proposed Design matrix and results of experimental runs Experimen
t No. A B C
Tool Rotatio n Speed
(rpm)
Tool Travel Speed (mm/min)
No. of passes
1 710 20 1
2 710 28 2
3 710 40 3
4 1000 20 2
5 1000 28 3
6 1000 40 1
7 1400 20 3
8 1400 28 1
9 1400 40 2
Table 3.5 Taguchi employees also include the estimated SN ratios for hardness.[42]
3.2 Table Analysis of Variance Source DF Seq
SS Contr ibutio n
Adj SS Adj
MS F- Va lu e
P- Value
Tool Rotation Speed (rpm) *
2 39.26
6 24.20
% 39.2
657 19.
632 8
42 .1 3
0.023
Tool Travel Speed (mm/min)***
2 62.39
4 38.45
% 62.3
943 31.
197 1
66 .9 4
0.015
No. of
passes ** 2 59.67 3
36.77
%
59.6 726
29.
836 3
64 .0 2
0.015
Error 2 0.932 0.57
%
0.93 21
0.4 660
Total 8 162.2
65 100.0 0%
Model Summary
S R-sq R-sq(adj) PRESS R-sq(pred) 0.682664 99.43% 97.70% 18.8742 88.37%
Since the Taguchi method cannot assess the impact of individual parameters on the overall operation, ANOVA is used to calculate the percentage contribution of each individual parameter in MINITAB 18.
Where DF-Degree of Freedom, Seq SS - Sequential Sum of Squares, Adj SS-Sum of Squares, Adj MS - Adjusted Mean The squares, F- as a statistical parameter, P- percent and *** & ** represent the most important and significant parameters and as * Is less important.
Statistically, F-test decides whether the parameters are significantly different. A larger F value shows the greater impact on the machining performance characteristics [15]. Larger F values are observed for Tool Travel Speed (mm/min) (P=0.015) (66.94%)
Table 3.7 shows the ANOVA result for impact hardness. It is observed that the Tool Travel Speed (mm/min),
Tool Rotati on Speed
(rpm)
Tool Travel Speed (mm/mi
n)
No. of passe
s Hardness SNRA1 MEAN1
710 20 1 79.8333 38.0437 79.8333
710 28 2 77.3333 37.7673 77.3333
710 40 3 72.5 37.2068 72.5
1000 20 2 85.3333 38.6224 85.3333
1000 28 3 72.6667 37.2267 72.6667
1000 40 1 75.6667 37.5781 75.6667
1400 20 3 73.3333 37.306 73.3333
1400 28 1 70.8333 37.0047 70.8333
1400 40 2 74.6667 37.4625 74.6667
(P=0.015) (66.94%) is most influences the hardness followed by No. of passes (P=
0.015) (64.02%) and least significant of Tool Rotation Speed (rpm) (A)(P=0.023 (42.13%).In the present study parameters are significantly factor for the hardness.
4 NUMERICAL OPTIMIZATION
The purpose of this research is to find the best parametric settings to achieve maximum impact strength of welded specimens at the same time, which is ideal for good welded joint efficiency. The desirability analysis is used to determine the best parametric setting to obtain the absolute Hardness of the welded specimens.
The Stir welding process is optimized using the Minitab18 program. The common steps and procedures that are followed in the Minitab software are described in detail here. The results of multi-objective optimization for Hardness are shown in fig.
7. Optimal Hardness : and 85.3333 (J) has been obtained at(a) Tool Rotation Speed (rpm) A2 1000 rpm (b) Tool Travel Speed (mm/min), B1 20 mm/min (c) No. of passes B2 The mixed desirability factor D has a value of 1.000.
Fig. 4.1 Optimization results of Hardness by RSM
4.1 Confirmation test
The optimization results obtained have been validated by performing confirmatory experiments. Table 4.1 represents the results of confirmatory tests that are conducted in optimal conditions. It is seen from the table that the error in terms of percentage between the estimated and experimental results is very small and is less than 1%. This indicates that the optimized Stir welding process parameters higher NTS and UTS of Brass can be obtained. Three fresh experiments are
conducted for confirmation of models Eqs.
(3) And (4), with achieved optimal values of cutting parameters. The average of measured values for Tool Rotation Speed (rpm) A2 1000 rpm (b) Tool Travel Speed (mm/min), B1 20 mm/min (c) No. of passes . The accuracy of the models is analyzed on the basis percentage error. These errors are found is less than 1%during machining which affects the measurement techniques.
Since the error is less than10%, it is evidently proved that there is a good agreement between experimental and predicted values [38].
Table 4.1 Multi-objective optimization results
Optimal Control Parameters
Level Optimal
Level Experim
ental Predic ted (RSM)
Error (%) Welding
Current (A) A A2 B1 B2 85.3333 85.333
3 0
Gas flow rate
(L/min) B
Welding
Speed (mm/s) C 5. CONCLUSION
1. Taguchi‟s orthogonal array has been successfully used to find the optimum level setting of process parameters.
2. The optimum process parameters levels which are found to achieve greater hardness are such, The average of measured values for Tool Rotation Speed (rpm) A2 1000 rpm (b) Tool Travel Speed (mm/min), B1 20 mm/min (c) No. of passes result a maximum hardness values.
3. ANOVA result for hardness. It is observed that the Tool Travel Speed (mm/min), (P=0.015) (66.94%) is most influences the hardness followed by No. of passes (P= 0.015) (64.02%) and least significant of Tool Rotation Speed (rpm) (A)(P=0.023 (42.13%).In the present study parameters are significantly factor for the hardness.
4. Contour plots are drawn to study the interaction effect of the welding speed and the rotational speed on the Hardness of the friction stir welded joints.
5. Response surface methodology (RSM) is found to be very helpful in the process of optimization carried out in the present study. Here the predicted value obtained from the
models is very near to the experimental value.
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