monitoring of friction stir welding process … · plates in butt joint configuration. tool...

5th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12th–14th, 2014,

IIT Guwahati, Assam, India

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MONITORING OF FRICTION STIR WELDING PROCESS THROUGH

SIGNALS ACQUIRED DURING THE WELDING

Bipul Das1, Sukhomay Pal2, Swarup Bag3*

1 RESEARCH SCHOLAR, IITG, 781039, EMAIL: [email protected] 2 ASSISTANT PROFESSOR, IITG, 781039, EMAIL: [email protected]

3* ASSISTANT PROFESSOR, IITG, 781039, EMAIL: [email protected]

Abstract

Friction stir welding (FSW) patented in the year 1991, has not yet reached its full potential. Because it is a new

process and physical models are lacking in the field of FSW which makes it difficult to assure the weld quality.

The difficulty arises from the lack of data and monitoring processes regarding the influencing factors that

govern the welding process. In the current work, a converted milling machine developed for friction stir welding

is used to perform welding operations. Welding is carried out on 6 mm thick AA1100 series Aluminium alloy

plates in butt joint configuration. Tool rotational speed and welding speed are the two process parameters

considered for the experiments varied in three levels as 815, 1100, 1500 rev/min and 63, 98, 132 mm/min

respectively. Current and voltage signals for spindle motor and feed motor are acquired along with the signal of

rotational speed of the spindle. All the signals are acquired at 10 kHz sampling rate using a high speed data

acquisition system using MATLAB®

. Ultimate tensile strength and yield strength of the welds are measured and

these are tried to correlate with the root mean square values of the signals obtained during welding along with

the process parameters.

Keywords: FSW, monitoring, signals, regression

1 Introduction

Friction stir welding (FSW) is a solid state

joining process where the materials are not melted for

joining. FSW process was invented in TWI, UK in

the year 1991(Thomas et al., 1991). The FSW

process being an environmentally friendly process as

no fumes generated during welding as in case of

conventional arc welding processes, less power

consumption and applicability in case of difficult to

weld materials such as Aluminium, help this process

to grow its popularity over the world of joining.

Moreover as no filler material is required that reduces

the weight of the structure being welded which is

advantageous for light weight applications. In FSW

less sample preparation required for welding and the

sample to be welded need not to be pre-processed for

the removal of oxide layer, thus making this process

more effective and less time consuming. Industries

are moving towards this process and started

implementing it over other joining processes. FSW

found its application in numerous fields like

aerospace, automotive, railway industry and other

industrial sectors to name a few.

In FSW process a rotating tool is plunged

into the work piece material along the joint line. After

plunging it is kept for some time in that condition.

This time period is called the dwell time. Then the

tool being plunged into the workpiece is traversed

along the joint line. After the welding is complete the

tool is retracted back.

Process parameters involved in FSW process

are tool rotational speed, welding speed, tool tilt

angle, plunging depth, tool pin diameter, length and

profile. Among these parameters tool rotational

speed, welding speed and plunging depth are most

influencing (Mishra and Ma, 2005).

The main component of heating in FSW

comes from the friction between the shoulder and the

workpiece. This heat is again aided by adiabatic

heating due to severe plastic deformation of material

due to the stirring action of the tool. As the tool

rotates and moves inside the workpiece the material

in front of it gets heated which can be easily

plasticized by the tool. The flow of material in FSW

is difficult to understand as the physics of the

problem is still not clear. Many researchers have

studied material flow behaviour in FSW which can be

found in the literature (Reynolds 2000, Guerra et al.

2001, Colligan 1999, London et al 2003, Ouyang and

Kavacevic 2002).

As the physics of FSW process is not clear

and it involves many process parameters and the

effect of which cannot be studied separately so only

relying on process parameters for monitoring the

process is not sufficient. Apart from the process

MONITORING OF FRICTION STIR WELDING PROCESS THROUGH SIGNALS ACQUIRED DURING THE WELDING

parameters signals acquired during the process can

also play an effective role in monitoring of the FSW

process. Force signals, current and voltage

(2008), Lin and Ting (1995), Melendez (2003) used

force signals for monitoring FSW process using

dynamometers. The force associated with FSW

process are the vertical force (z-direction),

force (y-direction) and side force (x-direction). Most

of the researchers commented that among the three

forces, z-direction force and y-direction force are

more important than the x-direction force

the force signals, acoustic signals were also acqu

by many researchers during FSW process. Zeng et al.

(2006) used acoustic emission signal for studying the

effect of tool wear in FSW process. Chen et al.

(2003) also acquired acoustic emission signals during

FSW process. They used wavelet analysis for

processing. From the signals they commented that the

material flow is different in advancing side and

retreating side of the weld as the patters of the

acquired signals were different. Ramulu et al. (2013)

developed a criterion to find out onset of

formation based on force signals data acquired during

FSW process.

The published literature in the field of FSW

process for monitoring the process through signal

processing is less so the current study involves the

monitoring of friction stir welding process through

signals acquired during the welding process. In the

current study four signals were acquired during the

welding process namely current and voltage signals

of the main spindle motor, current signal from

motor and tool rotational speed signal. All the signals

were acquired using high speed data acquisition

system (NI-USB-6259) at a sampling frequency of 10

kHz. MATLAB is used for acquisition of the signals.

Root mean squared (RMS) values of the signals are

obtained which is along with the process parameters

used for developing regression models. The

regression models are developed for correlating the

signal information and the process parameters with

ultimate tensile strength (UTS) and yield strength

(YS) of the joints.

2 Experimental studies

2.1 Experimental setup

In the present work, 6 mm thick AA1100

Aluminium alloy plates (110mm×160mm×6mm)

were used as the work piece. The setup used for

welding is shown in Figure 1. The tool is made of

SS316 material and different dimensions associa

with the tool are shown in Figure 2. Eddy current

sensors are used for acquiring the current signals

from the main spindle motor and feed motor. Voltage

sensor is used for acquiring the voltage signal from

the main spindle motor between two phases of t

motor windings. Tool rotational speed signal is

acquired using noncontact type laser tachoprobe. The

calibration of current sensors are 10 mV = 1A and

that of rotational speed sensor is 1V = 1000 rev/min


gnals acquired during the process can

also play an effective role in monitoring of the FSW

process. Force signals, current and voltage

signals,and acoustic emission signals can be

monitoring the FSW process. Trimble et al. (2012),

Yang et al. (2008), Fleming et al.

(2008), Lin and Ting (1995), Melendez (2003) used

force signals for monitoring FSW process using

dynamometers. The force associated with FSW

direction), horizontal

direction). Most

of the researchers commented that among the three

direction force are

direction force. Apart from

acoustic signals were also acquired

by many researchers during FSW process. Zeng et al.

(2006) used acoustic emission signal for studying the

effect of tool wear in FSW process. Chen et al.

(2003) also acquired acoustic emission signals during

They used wavelet analysis for signal

processing. From the signals they commented that the

material flow is different in advancing side and

retreating side of the weld as the patters of the

Ramulu et al. (2013)

developed a criterion to find out onset of defect

formation based on force signals data acquired during

The published literature in the field of FSW

process for monitoring the process through signal

processing is less so the current study involves the

ng process through

signals acquired during the welding process. In the

acquired during the

current and voltage signals

the main spindle motor, current signal from feed

d signal. All the signals

h speed data acquisition

at a sampling frequency of 10

Hz. MATLAB is used for acquisition of the signals.

values of the signals are

the process parameters

regression models. The

for correlating the

signal information and the process parameters with

ultimate tensile strength (UTS) and yield strength

In the present work, 6 mm thick AA1100

Aluminium alloy plates (110mm×160mm×6mm)

used as the work piece. The setup used for

. The tool is made of

SS316 material and different dimensions associated

. Eddy current

sensors are used for acquiring the current signals

motor. Voltage

acquiring the voltage signal from

the main spindle motor between two phases of the

motor windings. Tool rotational speed signal is

acquired using noncontact type laser tachoprobe. The

calibration of current sensors are 10 mV = 1A and

that of rotational speed sensor is 1V = 1000 rev/min

(from experiment 1 to 3) and 1 V = 10000 rev/min

(from experiment 4 to 10)

Figure 1: FSW setup used for experiments

Figure 2: Schematic of the FSW tool

2.2 Experimental procedure

The design matrix to carry out the

experiments is obtained using Taguchi L9

shown in Table 1. Fourth experiment is repeated

which is chosen randomly.

parameters considered in the current study

rotational speed varied in three levels as 815, 1100,

1500 rev/min and welding speed

levels as 63, 98, 132 mm/min. A

tool as shown in Figure 2 with pin diameter o

and pin length of 5.4 mm is used in the experiments

In all the experiments plunging depth is kept fixed at

0.06 mm. The experiments are performed randomly.

Four signals viz. main sp

feed motor current signals are acquired using hall

effect current transducer, tool rotational speed signal

using non-contact type laser tachoprobe and voltage

signals are acquired using voltage sensor. Current

signals and voltage signals are acquired from one of

the three phases available in the respective three

phase induction motor for tool rotation and feed

motion of the tool.From each welding


164-2

acoustic emission signals can be used for

Trimble et al. (2012),

8), Fleming et al.

(from experiment 1 to 3) and 1 V = 10000 rev/min

: FSW setup used for experiments

Schematic of the FSW tool

The design matrix to carry out the

experiments is obtained using Taguchi L9 and is

Fourth experiment is repeated

randomly. The two process

current study are tool

varied in three levels as 815, 1100,

and welding speed varied in three

A straight cylindrical

with pin diameter of 6 mm

and pin length of 5.4 mm is used in the experiments.

In all the experiments plunging depth is kept fixed at

The experiments are performed randomly.

Four signals viz. main spindle motor and

feed motor current signals are acquired using hall

effect current transducer, tool rotational speed signal

contact type laser tachoprobe and voltage

signals are acquired using voltage sensor. Current

e acquired from one of

available in the respective three

phase induction motor for tool rotation and feed

From each welding sample three

5th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12th–14th, 2014, IIT

Guwahati, Assam, India

164-3

tensile specimens (ASTM E8M) and one hardness

specimen are prepared.

Table 1: Design matrix with an additional

experiment

Experiment No. Tool rotational

speed (rev/min)

Welding speed

(mm/min)

1 815 63

2 815 98

3 815 132

4 1100 63

5 1100 98

6 1100 132

7 1500 63

8 1500 98

9 1500 132

10 1100 63

2.3 Experimental observations

In all the welding no weld is found to have

any visual defects. Only excessive flash in all the

welding is observed may be due to high plunging. In

the Figure 3welded specimens obtained from

experiment no. 5 and 8 are shown as these two are the

cases of highest and lowest UTS respectively.Main

spindle motor current signal and tool rotational speed

signal acquired during experiment no. 5 and

experiment no. 8 are shown in the Figure 5.

Signals obtained during experiment no. 4

and 10 for comparison are shown in Figure 6 (a)-(d)

as these two experiments are conducted under same

process parameters.It is observed from the figures

that although the process parameters remain same but

the trends in both main spindle motor current and tool

rotational speed signal are not same. So it can be

observed that for monitoring the FSW process only

relying on process parameters will not be sufficient.

Vickers’s hardness values measured at the

middle of welded specimens obtained from

experiment no. 5 and 8 are shown in Figure 4.

Similar trends in the hardness profile are observed for

rest of the welded specimens.

The data as shown in Table 2areused to

obtain regression models for UTS and YS.

Regression is done using least square fit method. In

the case of UTS R-squared value is 0.9998 and same

for YS is found out to be 0.9781. In obtaining the

regression modeling RMS values of main spindle

motor current signal and tool rotational speed signal

are only used. The p values for all the coefficients are

found to be less than 0.05 for both the regression

models.

Figure 3: Welded specimens from experiment no.

5 and 8

��

� 438.842 0.526 � �� 1.618 ��

41.274 � ��_��_� � 0.162 � ��_��

� 0.060 � �� _��_� 0.00016 � ��

� ��_�� 0.311 �� _��_�

� 0.0013 ��

� ��_�� 1�

��

� 566.888 � 0.241 � �� 2.457 ��

� 57.001 � ��_��_� � 0.065 � ��_��

0.021 � �� _��_� 0.00004 � ��

� ��_�� 0.246 �� _��_�

� 0.00003 ��

� ��_�� 2�

Figure 4: Vickers’s hardness value of the welds

obtained from experiment no. 5 and 8


Figure 5: (a) Main spindle motor current signal obtained during experiment 5

obtained during experiment 5. (c) Main spindle motor current signal obtained during experiment 8. (d) Tool

rotational speed signal obtained during experiment 8


(a)

(b)

(c)

(d)

: (a) Main spindle motor current signal obtained during experiment 5 (b) Tool rotational speed signal


rotational speed signal obtained during experiment 8


164-4

(b) Tool rotational speed signal


5th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12

Guwahati, Assam, India

Figure 6: (a) Main spindle motor current signal from experiment 4 (b) Main spindle motor current signal

from experiment 10 (c) Rotational speed signal from experiment 4 (d) Rotational speed signal from

All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12

(a)

(b)

(c)

(d)

motor current signal from experiment 4 (b) Main spindle motor current signal


All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12th–14th, 2014, IIT

164-5

motor current signal from experiment 4 (b) Main spindle motor current signal



164-6

experiment 10

Table 2: Experimental runs with response

Experiment

No.

RPM

(rev/min)

WS

(mm/min)

RMS_RPM_C

(A)

RMS_WS_C

(A)

RMS_RPM_V

(V)

RMS_RPM

(rev/min)

UTS

(MPa)

YS

(MPa)

%

Elongation

1 815 63 10.07 0.86 408.50 783.1 78.59 49.17 19.64

2 815 98 09.61 0.65 410.30 835.0 80.36 44.67 19.58

3 815 132 09.25 0.56 393.80 834.7 83.00 45.48 19.35

4 1100 63 10.19 0.81 398.84 1122.6 86.18 56.39 17.08

5 1100 98 09.66 0.67 403.92 1128.9 87.46 51.97 16.89

6 1100 132 09.80 0.61 408.18 1101.2 84.22 53.68 18.81

7 1500 63 11.28 0.84 408.40 1520.9 81.75 47.35 20.84

8 1500 98 10.54 0.83 404.30 1532.0 67.67 47.98 09.80

9 1500 132 10.45 0.64 405.80 1537.0 79.67 45.27 20.34

10 1100 63 09.87 0.58 401.22 1131.0 84.80 49.59 19.98

RPM=tool rotational speed, WS=welding speed, RMS_RPM_C=RMS value of main spindle motor current signal, RMS_WS_C=RMS value of feed motor current

signal, RMS_RPM_V=RMS value of main spindle motor voltage signal, RMS_RP=-RMS value of tool rotational speed signal

3 Conclusion

In the current study four signals are

acquired during friction stir welding of Aluminium

alloy. Two regression models are obtained using

the experimental data for UTS and YS. The

regression model for UTS has a higher R-squared

value (0.9998) than the one for YS (0.9781).

Among different process parameters, the RMS

values of the main spindle motor current signal was

found to have least p-value (0.02), implies that the

information contained in the main spindle motor

current signal is more effective, which make this

signal more influencing compared to other signalsin

predicting UTS and YS using the developed

models.

References

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5th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12th–14th, 2014,

IIT Guwahati, Assam, India

164-7

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monitoring of friction stir welding process … · plates in butt joint configuration. tool...

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