ADAPTIVE CONTROLLER TO MINIMIZE POSITION …...Adaptive Controller to Minimize Position Disturbances of Tool Pin While Joining Aluminium ... shown in Table 1 using the powder metallurgy

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International Journal of Mechanical Engineering and Technology (IJMET)

Volume 9, Issue 10, October 2018, pp. 399–416, Article ID: IJMET_09_10_043

Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=9&IType=10

ISSN Print: 0976-6340 and ISSN Online: 0976-6359

© IAEME Publication Scopus Indexed

ADAPTIVE CONTROLLER TO MINIMIZE

POSITION DISTURBANCES OF TOOL PIN

WHILE JOINING ALUMINIUM METAL

MATRIX COMPOSITES BY FRICTION STIR

WELDING

Rahul SG, Kavitha P

School of Electrical Engineering

Vellore Institute of Technology, Vellore, Tamilnadu, India

ABSTACT

Friction Stir Welding is more feasible in joining of Aluminium Metal Matrix

Composites (AMMC) than fusion welding process which avoids the formation of

Intermetallic Compounds in weld zone. In this study, an adaptive controller is

developed for joining Al6061/SiC/B4C composites by Friction Stir Welding to address

the position disturbances of the rotating tool pin. The aluminium composites are

initially prepared with five different combinations of disperoids and are welded at

three level parameter settings followed by tensile testing. The obtained experimental

data are used to estimate the model of the plant by System Identification method.

Using the estimated model of the plant, PID controller and an adaptive controller are

developed to control the position of rotating tool pin during the occurrences of

disturbance. The study indicates that, PID controller results in a noise response while

the MRAC results in a smooth response. Experimental validation is done by welding

the AMMC samples using MRAC followed by microscopic studies. The results

emphasize that, MRAC results in superior performance by minimizing position

disturbances.

Key words: Friction Stir Welding, Aluminium, Composite, PID, Adaptive Controller.

Cite this Article: Rahul S G, Kavitha P, Adaptive Controller to Minimize Position

Disturbances of Tool Pin While Joining Aluminium Metal Matrix Composites by

Friction Stir Welding, International Journal of Mechanical Engineering and

Technology 9(10), 2018, pp. 399–416.

http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=9&IType=10

1. INTRODUCTION

In this study, Aluminium Metal Matrix Composites (MMC) are joined using FSW process.

Aluminium Metal Matrix Composites (AMMC’s) are widely preferred in designing aerospace

parts like as fuselage, fins, wings, etc and in automobile parts such as foot rest, brake drum,

head light [1] and [2]. AMMC holds good strength, less weight, strong hold after material

Rahul S G, Kavitha P


processing leading to a superior performance [3]. Fusion welding is not preferred for joining

AMMC’s due to the formation of Intermetallic Compound formation (IMC) due to complex

chemical reactions between metal matrix and the disperoids which makes the weldments

brittle [4]. In conventional days, riveting was used for joining of aerospace and automobile

components which was not an economical method [5] and [6]. Emergence of FSW has

altered this approach thereby saving energy, resources and cost. Friction Stir welding possess

several benefits compared to other fusion welding process such as; less workpiece distortion,

fine microstructure, good joinability, no shielding gas, no consumable materials, no surface

cleaning and decreased fuel consumption etc [6]. Some authors have presented brief review

on FSW process, joining of AMMC by FSW, the effects of process parameters influencing

the process variables ([7], [4], [8], [9] and [10]). Most of the works carried out on joining

Aluminium Metal Matrix Composites using FSW process involved welding at various input

parameter levels. Followed by, authors studied the tensile properties, analysed the

microstructure and Intermetallic Compound Layer formations using Scanning Electron

Microscopy ([11], [12], [13], [14], [15], [16], [17], [18], [19]). Some works were on

development of empirical equations between the interdependent parameters [20].

The important process parameters in FSW which can be controlled are; traverse speed,

rotational speed, axial force on workpiece, and contact angle between workpiece and tool

which affect the heat generation and strength of the weld zone [21]. The rotation of tool pin

causes the materials to undergo stirring and mixing. Faster tool pin rotation generates high

temperatures due to increased frictional heating leading to intense mixing of materials. An

optimal value of these parameters can result in efficient weldments [22].

Very few works have been carried out in control aspects of FSW process. Some of the

literature reports on control system for FSW are discussed as follows. Xin Zhao related the

process parameters (travel speed, rotation speed, plunge depth) to the process variables (path

and axial force) to develop empirical models. The steady state relationships were developed,

and dynamic studies were carried out by low order linear equations. Experimental results

validated the findings [23]. Xin Zhao et al., presented an FSW path force controller. A process

model was developed to relate the rotational speed of the tool. Smith Predictor Control was

developed using Pole Placement technique and the results were experimentally validated [24].

Paul A. Fleming presented his thesis on monitoring and controlling during FSW process. The

authors presented novel methods for gap detection during the process and automatic fault

detection techniques. The models were developed using MATLAB and the results were

experimentally validated [25]. Tyler A. Davis developed an observer based adaptive robust

control for controlling the axial force to eliminate model errors and disturbances. The

experimental validations showed that axial force vibrations were reduced drastically [26].

William R. Longhurst et al., developed a torque controller for FSW process. A Ziegler-

Nichols tuned PI controller was developed to control the toque with a standard deviation of

0.231 Nm. The authors suggested that controlling torque is beneficial than controlling force in

maintaining the tool depth and axial force [27]. Jeroen De Backer et al., developed a

temperature control system to modify spindle speed for maintaining the temperature using

thermoelectric effect. The results were experimentally validated by applying the control

system to a robotic FSW machine [28]. Tom J. Stockman et al., developed a thermal control

system for FSW process. The authors suggested to use induction coil for preheating the base

martials instead of depending on the frictional heat for controlling the temperature. A 2D heat

transfer model was developed and basic feedback control scheme was used in this study [29].

Jeroen De Backer developed a feedback control scheme for robotic FSW process. The

initial study was to understand the effects of welding forces on the accuracy of the robot and a

deflection model was developed for the robot. Then, a temperature control scheme model was

Adaptive Controller to Minimize Position Disturbances of Tool Pin While Joining Aluminium

Metal Matrix Composites by Friction Stir Welding


developed to control temperature by varying the tool rotation speed. The combinations of the

two models resulted in defect free weldments [30]. B.T. Gibson et al., presented a brief

review on FSW process, its control and automation. Aspects in robotic FSW and evaluation

methods for weld quality are also presented in the review article [31]. Nur’Amirah Busu et al.,

presented a review on controlling force in FSW process during joining aluminium alloys. In

this study, control of the axial force, travel force and torque were performed by varying the

FSW process parameters. The study also reflects the drawback s of the control strategies and

suggests the techniques to improve the weld quality [32]. Brandon Scott Taysom presented

his work on controlling temperature for FSW process by developing a Model Predictive

Control. The study was done by developing two models; first-order plus dead time model and

a heat source model. The MPC controller resulted in superior performance [33]. Nuno

Mendes et al., presented a review on control schemes applied in FSW process. The authors

necessitated the significance of fine tuning the FSW process parameters. The advantages and

disadvantages of few control schemes are discussed [34]. Shujin Chen et al., developed a

closed loop PID control-based Smith Predictor control system to control the temperature. The

PID controller tuning was done using ZN technique. The experimental results showed that the

control scheme was more feasible, and the tensile strength of the weldments were uniform

[35]. Markus Krutzlinger et al., developed a PI-temperature control system during dissimilar

metal joining (aluminium-copper) using FSW process. Six levels of temperature were used in

conducting the experiments and the study showed formation of IMC layer [36]. The welded

samples were examined using microscopic studies. It is understood that most studies were on

control of torque, force and temperature during FSW process. In the present study, the

position disturbances of rotating tool pin during FSW process of joining AMMC plates is

addressed by developing a Model Reference Adaptive Controller (MRAC). The performance

of the adaptive controller is compared with that of a standard PID controller followed by

experimental validations and microscopic studies.

2. EXPERIMENTATION AND METHODOLOGY

Aluminium Matrix Composites consisting Aluminium 6061 of 60-70%, Silicon Carbide (SiC)

0f 10-20% and 5-10% of Boron Carbide (B4C) are prepared at different compositions as

shown in Table 1 using the powder metallurgy process. Aluminium alloy 6061 powders are

mixed with crystalline SiC and B4C in ball mill for 15 min followed by air drying of the

powder blends. Subsequently, sintering of powder mixtures are done using plasma sintering at

950 °C with a pressure of 50 MPa. The dimensions of circular sintered samples are, 40 mm

radius and 5 mm thickness. Plates with dimension 150 × 100 × 5 mm are prepared and cut

into square plates. All the prepared samples at four different combinations of AMMC’s are

joined using FSW process setup available in Chennai as shown in

Figure 1. A high strength carbon steel cylindrical tool is used as shown in Figure 2.



Figure 1 Experimental setup of FSW process

Figure 2 High strength carbon cylindrical tool

Table 1 Samples Specification

S. No Weight % of the samples

Aluminium 6061 SiC B4C

Combo 1 70 20 10

Combo 2 73 19 8

Combo 3 77 17 6

Combo 4 80 15 5

Combo 5 75 16 9

Welding parameters controlling the process are; welding speed, spindle speed and plunge

depth. Each parameter is placed in three equally spaced values, coded at levels -1, 0, and +1.

The levels of welding parameters are shown in

Table 2.The welding for all four combinations of AMMC’s plates are performed at three

parameter levels as shown in

Table 2.

Table 2 Levels of welding parameters

All the weldments are cooled and cleaned with a wire brush. The samples are then

subjected to tensile test using an Instron 8801 series 100 KN capacity servo hydraulic testing

machine after preparing the samples at ASTM E8-04 standard (as shown in Figure 3).

Welding Parameters Level -1 Level 0 Level 1

Spindle speed (rpm) 700 900 1100

Welding speed (mm/sec) 0.8 1.3 1.8

Plunge depth (mm) 0.05 0.10 0.15 Welding Parameters Level -1 Level 0 Level 1



Plunge depth (mm) 0.05 0.10 0.15

Welding Parameters Level -1 Level 0 Level 1



Plunge depth (mm) 0.05 0.10 0.15




Figure 3 Samples of five combinations of Al6061/SiC/B4C cut for tensile test

The results of of Tensile strength and Yeild strength are presented in Table 3.

Table 3 Weld quality of the samples joined by experimental trials by NDT

AMMC

Combinations

Spindle Speed

(rpm)

Welding Speed

(mm/sec)

Plunge Depth

(mm)

Tensile Strength

(Mpa)

Yeild Strength

(Mpa)

Combo 1

(Al-70%, SiC-

20%, B4C-

10%)

700 0.8 0.05 102 87

900 1.3 0.1 104 89

1100 1.8 0.15 105

91

Combo 2

(Al-73%, SiC-

19%, B4C-8%)

700 0.8 0.05 99 85

900 1.3 0.1 100 86

1100 1.8 0.15 101 87

Combo 3

(Al-77%, SiC-

17%, B4C-6%)

700 0.8 0.05 111 90

900 1.3 0.1 115 92

1100 1.8 0.15 117 93

Combo 4

(Al-80%, SiC-

15%, B4C-5%)

700 0.8 0.05 91 85

900 1.3 0.1 94 88

1100 1.8 0.15 97 91

Combo 5

(Al-75%, SiC-

16%, B4C-9%)

700 0.8 0.05 109 89

900 1.3 0.1 112 90

1100 1.8 0.15 116 95

3. RESULTS AND DISCUSSIONS

The results of this study are presented in the following sections.

3.1. Estimation of Plant Model Using System Identification Technique

To design a position controller, the initial requirement is to obtain the plant model which is to

be controlled [37]. For systems with a known plant model, we can directly design a controller.



For unknown systems, mathematical model can be estimated using system identification

technique as shown in Figure 4.

From the literature [30] and [29], it is understood that, for the given variables ( ) and

( ) the transfer function model is First order plus dead time model (FOPTD) denoted by,

( )

( ) ( )

Where,

– Time constant of the FSW process

– Gain of FSW process

– Dead time of the process,

In s-domain the FOPTD model of FSW system is represented as,

( )

The available input-output experimental data in Table 3 is used in estimating the transfer

function of the plant using System Identification method with the help of MATLAB. The

transfer function obtained is second order type with two poles and one zero (shown in Figure

5) and is represented as;

( )

Using Pade approximation technique the second order transfer function is approximated as

FOPTD model with one pole (as shown in Figure 6) is given by,

( )

Import the experimental

data (Spindle speed, Weld

speed, Plunge depth and

Tensile Strength)

Merge all the

experimental

data

Working data

Estimation

type

Transfer

Function

model

Export to

workspace

Figure 4 Procedure involved in estimating the TF using System Identification




Figure 5 Pole-Zero plot of the second order plant model

Figure 6 Pole-Zero plot of the FOPTD plant model

3.2. Controller Synthesis

This section discusses the synthesis of two control schemes; a conventional PID controller

and an adaptive controller; Model Reference Adaptive Controller.

3.2.1. Standard PID Controller

The most standardised conventional controller is the PID controller which is fundamentally

used in most of the control applications. The PID controller transfer function is denoted by,

( ) ( )

Where,

– Proportional gain

– Integral gain

– Derivative gain

Tuning is a procedure for obtaining desired values of controller gains. The most common

tuning method is Zeigler Nichols method (ZN) [38]. With advancement in computation, auto

tuning is performed in MATLAB. The values controller of the tuned gains is; = -8.487,



= -1.548 and = -0.2063. The step response of the FOPTD model while using PID

controller is shown in Figure 7.

Figure 7 Step response of PID controller

3.2.2. Enhanced Model Reference Adaptive Controller

The conventional linear feedback controllers such as P, PI and PID controller do not hold

good performance in non-linear unstable processes due to to the variations in process

dynamics and external disturbances [39]. To cope up with such uncertainties, adaptive control

came into existence from the 1960s. The adaptive controller consists a feedback unit with

adjustable parameter system. After sensing the out of the plant, the error is sent to controller

block [40]. Also, an adaptation loop acts on the parameters to be adjusted during the

occurrence of disturbances. The adaptation loop works in finding optimal values for the

controller gains. The control signal from controller, output signal from plant and the desired

set point value are fed to the parameter updating block in the adaptation loop. Suitable

adaptation mechanism is used in updating the controller parameters. The MRAC control

scheme is shown in Figure 8.

ControllerActual plant

P

Adaptive

Mechanism

Reference

Model

yref

yp

Plant

output

error

Control

signalSetpoint

- -

Figure 8 The Model Reference Adaptive Control (MRAC) Scheme




The MRAC updates the controller parameters to change the response of the system

response to match a reference model. A second order reference model is preferred in the study

which is represented by,

The adaptation mechanism used in MIT rule. MRAC observes the error ( ) which is the

difference between the plant output ( ) and the reference model ( ).

The adaptive mechanism updates the controller parameters. A loss function ( ) is formed

from the observed error. In the loss function ( ), theta ( ) is the parameter that which is

adapted in the controller,

( )

( )

The MIT rule states that, the loss function must be minimised which is is achieved by

varying in opposite direction of the gradient which is denoted by,

Where, the learning rate (γ) is influenced by the control signal. Normalised adaptation

scheme is carried out in this study.

And

Normalised MIT tuning rule shown in above equation specifies the significance of

removing the difficulty of zero division for small values of , which is the change in error

with respect to ).

Figure 9 Step response of MRAC

The step response of MRAC is shown in Figure 9. It is observed that the settling occurs

faster in MRAC than that of the PID controller for the FSW system.

3.3. Validation of Position Disturbance Rejection



The performance analysis is carried for the developed PID and MRAC controllers by applying

asymmetric input patterns as position disturbances. The controller responds to the

disturbances and adapts to the applied patterns. A combination of sinusoidal signals is used to

form the asymmetric position disturbance ( ) which are mathematically denoted by,

( ) ( ( ) ( ) ( ) ( ))

Where,

- Equilibrium operating point applied when multiple sine signals are applied

– Magnitudes of the three sinusoidal signals

– Frequencies of the three sinusoidal signals

– Bias

The input pattern with and the frequencies, respectively are used in analysing the system behaviour. The validation is

done for 30 seconds and the responses of PID controller and MRAC are shown in Figure 10

and Figure 11 respectively.

Figure 10 Response of PID controller to the asymmetric position disturbances

Figure 11 Response of MRAC to the asymmetric position disturbances

It is observed that PID controller adapts to the sinusoidal position disturbance with more

noisy response. Whereas, the MRAC adapts to the disturbances with a smooth response and

there is no noise. Its is understood that MRAC results in superior disturbance rejection even

for small changes in position (here between 0.25 mm) that that of the PID controller.




Figure 12 Power Spectral Density of PID and MRAC

To validate the performance, the Root Mean Square Error (RMSE) value is computed for

the PID controller and MRAC respectively. It is found that, RSME value for PID is 0.2853

and for the MRAC, it is 0.2812. The Figure 12 shows the Power Spectral Density (PSD) of

the position controlling by PID controller and MRAC scheme. It is inferred that latter results

in lesser PSD than the former which indicates that MRAC is superior in providing position

control of the tool pin during disturbances. The same is validated experimentally in the

following section.

3.4. Experimental Validation during Friction Stir Welding of Aluminium

6061/SiC/B4C Composites

Tensile test is carried out to measure the tensile strength of the joined AMMC plates. The

optimal values of spindle speed, welding speed, and plunge depth are 1100 rpm, 1.8mm/sec

and 0.10 mm respectively which are achieved after series of experimental runs in attaining

defect free weldments. All the five combinations of samples are joined by implementing

MRAC position control scheme. The samples are cut as per ASTM E8-04 standard (as shown

in Figure 13) and are subjected to tensile test using Instron 8801 series 100 KN capacity servo

hydraulic testing machine (shown in Figure 14).

Figure 13 Samples of five combinations of Al6061/SiC/B4C subjected to tensile test



Figure 14 Universal Testing Machine for performing Tensile test

Table 4 Tensile test result of the AMMC combinations

AMMC Combinations UTS (MPa) Yield Strength (MPa) Location of failure

Combo 1

(Al-70%, SiC-20%, B4C-10%)

106

92

Stir Zone

Combo 2

(Al-73%, SiC-19%, B4C-8%)

102

87

Stir Zone

Combo 3

(Al-77%, SiC-17%, B4C-6%)

118

94

Heat Affected Zone

Combo 4

(Al-80%, SiC-15%, B4C-5%)

96

90

Stir Zone

Combo 5

(Al-75%, SiC-16%, B4C-9%)

115

95

Heat Affected Zone

The failure zones are observed in the locations namely; Weld Zone, Heat Affected Zone

and Stir Zone. From the Table 4, it is understood that failure mainly occurs in Stir zone for

AMMC combinations (combo 1, Combo 2 and Combo 4) on the advancing side of the

weldment which is due to lack of penetration and localized defects and flaws during stirring.

Failure at stir zone indicates bad bonding of the weldments. Thereby, the position control of

tool pin is good while implementing MRAC even during the presence of small position

disturbance. Among the five combinations of AMMC’s, higher strength is observed for the

Combo 3 (Al-77%, SiC-17%, B4C-6%) and Combo 5 (Al-75%, SiC-16%, B4C-9%) and their

respective failure occurs only at TMAZ.

3.5. Microscopic Studies

Microscopic examinations are done for the validated sample of combo 3 using SEM. The

different zones examined are; microstructure of base material, stir zone and heat affected

zone. The image in Figure 15 shows wavery pattern which represents the brittle fractured




portion of HAZ. The fracture occurred due to insufficient heating which could be a result of

insufficient stir time or plunge depth at the HAZ.

Figure 15 Brittle fractured region of HAZ

The Figure 16 represents the SEM image of the base material. It is observed that, there is a

homogeneous dispersion of SiC and B4C in the composite. Few traces of burr are observed in

some regions. There is no segregation of particles in the composite plates.

Figure 16 SEM image of base material

The Figure 17 and Figure 18 shows the optical microscopic image and SEM image of Stir

Zone respectively. Onion ring patterns are observed in the SEM image. During stirring,

particle re-arrangement occurs. Onion ring patterns are formed due to a geometric effect in

which the cylindrical sheets of base materials are extruded and cut during tool rotation. The

Figure 19 and Figure 20 shows the EDAX mapping and elemental distribution of Al, C, O, Si,

K and Fe at the Stir Zone. The major constituents are 61.25 % Aluminium, 19.97 %Carbon

and 16.50 % Oxygen. A defect free bonding is observed from the pattern indicating good

bonding.



Figure 17 Stir Zone with good bonding

Figure 18 SEM image of stir zone

Figure 19 EDAX mapping at Stir Zone




Figure 20 Elemental distribution at Stir Zone

4. CONCLUSIONS

In this study, an adaptive controller (MRAC) is developed to control the position disturbances

of tool pin during Friction Stir Welding of Al6061/SiC/B4C Composite plates prepared at

different composition 60-70%, 10-20% and 5-10% respectively. The following conclusions

are made;

After welding, AMMC plates at three parameter settings tensile test is done. The experimental

data is used to estimate the plant transfer model using System Identification technique which

is a First Order Plus Dead Time (FOPTD) transfer function model.

A PID controller and MRAC are developed for the estimated model. The PID controller

parameters are obtained by auto tuning and the parameters of MRAC are updated using

modified MIT rule.

When both the controllers are tested for asymmetric position disturbances of tool pin, PID

resulted in noise response while MRAC adapted to the disturbances and resulted in a smooth

response curve.

Performance validation for both controllers are done by computing the Root Mean Square

Error which is 0.2853 for PID and 0.2812 for MRAC. Also, it is inferred that MRAC results in

lesser Power Spectral Density

Experimental validation is carried out by implementing MRAC during FSW of AMMC plates

of all combinations at optimal parameter settings (1100 rpm spindle speed, 1.8 mm/sec

welding speed, and 0.10 mm plunge depth respectively). Followed by, the samples are

subjected to tensile test. It was observed that, higher strength of 118MPa is observed for the

Combo 3 (Al-77%, SiC-17%, B4C-6%)

The microstructural studies of Combo 3 reveal that there is homogeneous dispersion of SiC

and B4C in the combo 3 composite. Few traces of burrs are observed in the base material. The

stir zone is uniform with onion ring patters indicating a good bonding. The EDAX mapping at

stir zone shows the presence of 61.25 % Aluminium, 19.97 %Carbon and 16.50 % Oxygen as

major constituents.

ACKNOWLEDGEMENT

The authors would like to thank Professor Dr. S. Arungalai Vendan and Professor Ashraff Ali

for their extensive support and suggestions for conducting experimentation.



REFERENCES

[1] Dasgupta, R., 2012, “Aluminium Alloy-Based Metal Matrix Composites: A Potential

Material for Wear Resistant Applications,” ISRN Metall., 2012, pp. 1–14.

[2] Sharma, R., P, S. J., Kakkar, K., Kamboj, K., and Sharma, P., 2017, “A Review of the

Aluminium Metal Matrix Composite and Its Properties,” (February), pp. 832–842.

[3] Ramnath, B. V., Elanchezhian, C., Annamalai, R. M., Aravind, S., Sri, T., Atreya, A.,

Vignesh, V., and Subramanian, C., 2013, ALUMINIUM METAL MATRIX COMPOSITES-

A REVIEW.

[4] Salih, O. S., Ou, H., Sun, W., and McCartney, D. G., 2015, “A Review of Friction Stir

Welding of Aluminium Matrix Composites,” Mater. Des., 86, pp. 61–71.

[5] Ali, A., Suban, K., Perumal, M., Ayyanar, A., Arungalai, &, and Subbiah, V., 2017,

“Microstructural Analysis of B 4 C and SiC Reinforced Al Alloy Metal Matrix Composite

Joints.”

[6] Mishra, R. S., and Ma, Z. Y., 2005, “Friction Stir Welding and Processing,” Mater. Sci.

Eng. R Reports, 50(1–2), pp. 1–78.

[7] Gan, Y. X., Solomon, D., and Reinbolt, M., 2010, “Friction Stir Processing of Particle

Reinforced Composite Materials,” Materials (Basel)., 3(1), pp. 329–350.

[8] Ana, B., Adrian, C., Eduard, N., Monica, I., and Alin, R., 2015, “Friction Stir Welding of

Composite Materials with Metallic Matrix : A Brief Review,” pp. 449–454.

[9] Arya, P. K., 2015, “A Review on Friction Stir Welding for Aluminium Alloy Composite,”

Int. J. Res. Appl. Sci. Eng. Technol., 3(6), pp. 216–221.

[10] Vijaya Ramnath, B., Abhishek Subramanian, S., Rakesh, R., Sharun Krishnan, S., and

Ashwin Ramanathan, A. L., 2018, “A Review on Friction Stir Welding of Aluminium

Metal Matrix Composites,” IOP Conf. Ser. Mater. Sci. Eng., 390(1).

[11] Bozkurt, Y., Kentlİ, A., Uzun, H., and Salman, S., 2012, “Experimental Investigation and

Prediction of Mechanical Properties of Friction Stir Welded Aluminium Metal Matrix

Composite Plates,” 18(4), pp. 2–6.

[12] Sailaja, C., and Reddy, B. R. G., 2016, “Fabrication and Friction Stir Welding Of,” pp.

13153–13159.

[13] Abegunde, O. O., Akinlabi, E. T., and Madyira, D. M., 2016, “Force Feedback Analysis:

Friction Stir Welding of Aluminium Metal Matrix Composite,” Lect. Notes Eng. Comput.

Sci., 2224.

[14] Nallabelli, A. B., Banoth, B. N., Ravi, B., and Raja, G., 2017, “Fabrication and Friction

Stir Welding of Aluminum 7075 / Wc Metal Matrix Composites,” pp. 367–374.

[15] Sadashiva, M., Shivanand, H. K., and Vidyasagar, H. N., 2018, “Characteristic Evaluation

of Process Parameters of Friction Stir Welding of Aluminium 2024 Hybrid Composites,”

020054, p. 020054.

[16] Das, B., Pal, S., and Bag, S., 2017, “Weld Quality Prediction in Friction Stir Welding

Using Wavelet Analysis,” Int. J. Adv. Manuf. Technol., 89(1–4), pp. 711–725.




[17] Das, B., Bag, S., and Pal, S., 2017, “Probing Weld Quality Monitoring in Friction Stir

Welding through Characterization of Signals by Fractal Theory,” J. Mech. Sci. Technol.,

31(5), pp. 2459–2465.

[18] Selvaraj, G., and Karthikeyan, T., 2018, “Microstructure and Mechanical Properties Study

on Aluminum Metal Matrix Composites with Boron Nitride by Friction Stir Welding,” J.

Comput. Theor. Nanosci., 15(3), pp. 793–797.

[19] Tornem, V., Pri, P., and Varilnih, N., 2011, “Weldability of Metal Matrix Composite

Plates By Friction Stir Welding At Low Welding Parameters,” 45(5), pp. 407–412.

[20] Dinaharan, I., Murugan, N., Nadu, T., and Nadu, T., 2011, “Influence of Friction Stir

Welding Parameters on Sliding Wear Behavior of AA6061 / 0-10 Wt .% ZrB 2 in-Situ

Composite Butt Joints,” 10(14), pp. 1359–1377.

[21] Bodake, S. N., 2017, “Review Paper on Optimization of Friction Stir Welding Process

Parameters,” 10(1), pp. 611–620.

[22] He, X., Gu, F., and Ball, A., 2014, “A Review of Numerical Analysis of Friction Stir

Welding,” Prog. Mater. Sci., 65, pp. 1–66.

[23] Zhao, X., Kalya, P., Landers, R. G., and Krishnamurthy, K., 2007, “Empirical Dynamic

Modeling of Friction Stir Welding Processes,” pp. 27–36.

[24] Zhao, X., Kalya, P., Landers, R., and Krishnamurthy, K., 2009, “Path Force Control for

Friction Stir Welding Processes,” pp. 1–8.

[25] Fleming, P. a., 2009, “Monitoring and Control in Friction Stir Welding.”

[26] Davis, T. a., Shin, Y. C., and Yao, B., 2010, “Observer-Based Adaptive Robust Control of

Friction Stir Welding Axial Force,” 2010 IEEE/ASME Int. Conf. Adv. Intell.

Mechatronics, pp. 1162–1167.

[27] Longhurst, W. R., Strauss, A. M., Cook, G. E., and Fleming, P. A., 2010, “Torque Control

of Friction Stir Welding for Manufacturing and Automation,” Int. J. Adv. Manuf.

Technol., 51(9–12), pp. 905–913.

[28] De Backer, J., Bolmsjö, G., and Christiansson, A.-K., 2014, “Temperature Control of

Robotic Friction Stir Welding Using the Thermoelectric Effect,” Int. J. Adv. Manuf.

Technol., 70(1–4), pp. 375–383.

[29] Stockman, T. J., Cobb, J. B., Schneider, J., and Walker, B., 2014, “Thermal Control of the

Friction Stir Welding Process,” Therm. Process Model. - Proc. from 5th Int. Conf. Therm.

Process Model. Comput. Simulation, ICTPMCS 2014, (June), pp. 218–222.

[30] Backer, J., 2014, Feedback Control of Robotic Friction Stir Welding.

[31] Gibson, B. T., Lammlein, D. H., Prater, T. J., Longhurst, W. R., Cox, C. D., Ballun, M.

C., Dharmaraj, K. J., Cook, G. E., and Strauss, A. M., 2014, “Friction Stir Welding:

Process, Automation, and Control,” J. Manuf. Process., 16(1), pp. 56–73.

[32] Busu, N., Jaffarullah, M. S., Low, C. Y., Shaari, M. S. B., Armansyah, and Jaffar, A.,

2015, “A Review of Force Control Techniques in Friction Stir Process,” Procedia

Comput. Sci., 76(Iris), pp. 528–533.



[33] Taysom, B. S. B., 2015, “Temperature Control in Friction Stir Welding,” Temp. Control

Frict. Stir Weld. Using Model Predict. Control, 1(September 2015), pp. 1–36.

[34] Mendes, N., Neto, P., Loureiro, A., and Moreira, A. P., 2016, “Machines and Control

Systems for Friction Stir Welding: A Review,” Mater. Des., 90, pp. 256–265.

[35] Chen, S., Li, H., Lu, S., Ni, R., and Dong, J., 2016, “Temperature Measurement and

Control of Bobbin Tool Friction Stir Welding,” Int. J. Adv. Manuf. Technol., 86(1–4), pp.

337–346.

[36] Krutzlinger, M., Marstatt, R., Costanzi, G., Bachmann, A., Haider, F., and Zaeh, M. F.,

2018, “Temperature Control for Friction Stir Welding of Dissimilar Metal Joints and

Influence on the Joint Properties,” Key Eng. Mater., 767 KEM, pp. 360–368.

[37] Rincon, A., and Angulo, F., 2012, “Adaptive Control for Nonlinear Systems with Time-

Varying Control Gain,” J. Control Sci. Eng., 2012, pp. 1–9.

[38] Dhivyasri, G., Rahul, S. G., Kavitha, P., Arungalai Vendan, S., Ramesh Kumar, K. A.,

Gao, L., and Garg, A., 2018, “Dynamic Control of Welding Current and Welding Time to

Investigate Ultimate Tensile Strength of Miab Welded T11 Tubes,” J. Manuf. Process.,

32, pp. 564–581.

[39] Rahul, S. G., Dhivyasri, G., Kavitha, P., Arungalai Vendan, S., Kumar, K. A. R., Garg, A.,

and Gao, L., 2018, “Model Reference Adaptive Controller for Enhancing Depth of

Penetration and Bead Width during Cold Metal Transfer Joining Process,” Robot.

Comput. Integr. Manuf., 53, pp. 122–134.

[40] Li;, J. H. K., 2012, “Adaptive Control for a Class of Nonlinear System with Redistributed

Models,” J. Control Sci. Eng., 12(5).

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