potential application of acoustic emission technique …jestec.taylors.edu.my/vol 14 issue 3 june...

Journal of Engineering Science and Technology Vol. 14, No. 3 (2019) 1344 - 1360 © School of Engineering, Taylor’s University

1344

POTENTIAL APPLICATION OF ACOUSTIC EMISSION TECHNIQUE FOR WELD STRUCTURE INTEGRITY

MONITORING UNDER DYNAMIC LOADING

S. V. RANGANAYAKULU1,*, RAMESH KUMAR BUDDU2, P. V. SASTRY3

1School of Engineering, Guru Nanak Institutions Technical Campus,

Ibrahimpatnam, Hyderabad-501506, India 2FRMDC Division, Institute for Plasma Research, Bhat, Gandhinagar-382428, India

3Research and Development Division, Bharat Heavy Electricals Limited,

Hyderabad-500035, India

*Corresponding Author: [email protected]

Abstract

Acoustic Emission (AE) has emerged as a potential assessment technique for

structural health monitoring and integrity inspection in nuclear components and

weld structures during in-service conditions. The present paper gives the

experimental results of Acoustic Emission monitoring technique applied to

welded specimens made out of AISI SS 316L steel, under dynamic stress

conditions roughly simulating the pre-failure conditions in the welded structures.

The weld samples are fabricated by deliberately implanting different types of

weld defects like porosity, slag and inclusions in the welded zone. These samples

are subjected to dynamic loading precisely converging on the welded zone, with

the help of a specially fabricated mechanical jig with a load cell. AE output

parameters like the number of counts, energy, amplitude, frequencies are

monitored during loading of the samples (until the physical appearance of failure)

and it is observed that samples with defective welds gave a distinctly higher yield

of AE parameters compared to sample without defects.

Keywords: Acoustic emission, Calibration, Fusion reactor, Welded structures.

Potential Application of Acoustic Emission Technique for Weld Structure . . . . 1345

Journal of Engineering Science and Technology June 2019, Vol. 14(3)

1. Introduction

The structural integrity of the nuclear components in case of nuclear reactors (both

fission and fusion) is of very serious concern as their failures can lead to the reactors

breakdown and affect the operational activities. Austenitic stainless steel materials

are widely used for the fabrication of various structural components for nuclear

reactor subsystems due to their superior mechanical properties, fatigue and

corrosion resistance properties at elevated temperatures. The components are

joined by various joining techniques to develop the structural components for

various subsystems development like vacuum vessel, supports structures and

pipeline components, etc. The weld joints, in general, comprise of weld defects and

residual stresses if the proper care is not taken during the welding process.

If these components are under substantial mechanical and thermal load

conditions, the localized stress releases within the material propagate and cause

micro cracks in the structure. The structures like nuclear, pressure vessels and

chemical plants during operation experience severe thermal, structural gradients,

which affect the components strength and cause catastrophes. Acoustic Emission

(AE) technique has evolved as the potential diagnostic tool for the structural health

monitoring and fault finding events in several nuclear installations like in for

fusion, fission reactor components and pressure vessels [1-7]. Acoustic emissions

(AE) are elastic stress waves generated by a rapid release of energy from localized

sources within material under stress or repeated loading. Cao et al. [8] explained

that acoustic emissions often originate from material defect related sources such as

microscopic deformation or microcrack generation/propagation within the material

due to external loads application. The generated Acoustic Emission signals in the

form of sound can be effectively tailored to detect the fault sources, detection of

fracture or failure of structures by employing proper transducers. One of the

advantages compared to other NDE techniques is the possibility to observe damage

processes during the entire load history without any disturbance to the specimen.

The basis for quantitative methods is a localization technique to extract the source

coordinates of the AE events as accurately as possible. The preventive maintenance

and structure health integrity monitoring mechanism for operational components

and instruments is highly desirable to avoid any kind of catastrophe failures, which

are detrimental for the operational scenarios of the nuclear reactors or pressure

vessels [9-11].

AE detection process uses various analysed process parameters recording like

hits, amplitude, energy, frequency and rise time, etc., to measure the system

response against the load conditions. According to Hase et al. [12] and Deuster

et al. [13], by appropriate calibration and utilisation of the analysis techniques,

the defects can be correlated with linear localised techniques. Droubi et al. [14]

reported that recently, the non-contact method was used by sending guided

acoustic waves with laser beam source to detect the weld defects from long

distance weld pipes, which is a potential demonstration AE by nondestructive

measurements. The present study has demonstrated the applicability of the

acoustic emission application and potential response characteristics under

dynamic load condition on welded plates with different type weld defects. The

study focused on the demonstration and feasibility of applying AE technique for

preventing failures or diagnosing the enhanced defects in joints of structure

components when subjected to the dynamic mechanical loading circumstances,

which are rational in nuclear installations.

1346 S. V. Ranganayakulu et al.


2. Experimental Procedure

2.1. Materials

AISI SS316L plates of dimensions 70 mm ×16 mm × 10 mm are chosen as the

experimental materials for the study and are joined by manual metal arc welding

process and Tungsten Inert Gas (TIG) welding process. Single V groove with 600

bevel angle and 1 mm root height, a good finish is made before joint preparation.

The weld current of ~ 200 A and voltage of 12 V DC is used in the joining process.

ER 316L filler rods of 2.5 mm diameter and ~ 4-5 weld passes are used for the

welding. The weld joints of the final size of 140 mm × 16 mm × 10 mm are prepared

to study the defects response upon load. Weld zone of ~ 20 mm is maintained in all

the weld samples. Different types of weld defects are introduced in the welded zone

deliberately in the samples during the welding process. Three categories of samples

are fabricated as good weld plates (no weld defects), with porosity and slag

inclusions in the welded zones. Each set of 5 samples are tested for repeatability

check. The weld joints quality has been analysed with X-ray radiography to spot

the defects in the welds. The radiography images of different weld samples with

and without defects are shown in Fig. 1 for details.

Fig. 1. Weld defects (slag and porosity) samples

with X-ray radiography result.

2.2. Dynamic load cell for characterisation

The dynamic loading test of the samples has been carried out by using a specially

designed mechanical jig system, which is shown in Fig. 2. This has an arrangement in

such a way that the location of welded zone of the sample is subjected to the localised

force in a uniform manner and the fracture of the sample can take place after the plastic

deformation under an applied force. The technique is similar to the three-point bend

testing of the welded samples. This process is carried by keeping the welded samples

placed on two anvils and compressed at the centre with a mandrel. The mandrel is

pushed uniformly by bending movement at the centre where the weld zone is located.

The details of the test mechanical jig unit and with samples are shown in Figs. 2 (a) and

(b) for reference.



(a) Mechanical jig unit. (b) Mechanical jig system

with sensors and samples.

Fig. 2. Specially designed mechanical jig system.

2.3Acoustic emission set-up and calibration process

The dynamic load characteristics analysis has been carried out by using a two-

channel Acoustic emission system (Physical Acoustics Corporation, Model:

SB141; USA), which consists of two sets of AE sensors attached to preamplifier

and amplifier and measurement data analysis software by AE Win soft to analyse

the events in form of counts, rise time, energy, events, frequency and amplitude as

output parameters. The schematic of the measurement scheme is shown in Fig. 3.

Fig. 3. Calibration scheme of AE set up measuring system.

The standard calibration of the weld sample without any weld defect has been

carried out. The velocities are changed by the variation in frequency. The

calibration helps in identifying the generated frequencies during the acoustic events

during the loading conditions. The calibration with two sensors AE system



attachment has been carried to establish characteristics of the transducer used with

reference to the load exposed and the accurate location mapping to capture the

events of acoustic emission. Based on studies by Lee et al. [15], a frequency

response of a specific sensor with the load input can be expressed in terms of

velocity, displacement parameters, which is the most common representation of

calibration. During this process, the sensitivity of the sensor is established with the

given range of output frequency (acoustic emission) as per the input load.

According to Hase et al. [12], the dispersion curves using phase velocity and group

velocity of the tested weld plate material with the applied load tests with pencil lead

calibration technique are shown in Figs. 4(a) and (b), with the measured

frequencies, in which, is a similar procedure as reported [12]. This further confirms

the variation of the stress-energy released during the applied loading on to the weld

samples and events measured in terms of counts, energy, rise time and amplitude,

etc., with a known source.

(a) Phase velocities of SS316L weld sample during calibration.

(b) Group velocities of SS316L weld sample during calibration.

Fig. 4. Phase and group velocities

of SS316L weld sample during calibration.



3. Results and Discussion

The experiments are carried out with the given load to study the behaviour of

acoustic emission features in both the good and weld defect samples (porosity, slag

inclusions). The acoustic emission data was collected at the weld zone location by

applying stress load and detected data is processed in the form of events, counts,

amplitude, energy and frequency are recorded and analysed. As per Fig. 5, the load

applied in the weld zone of the good weld sample has shown in the form of events,

counts, energy and amplitude and cumulative counts with reference to the position

of the weld zone. The test plate undergoes slow stress form due to the applied load

and the AE monitoring system records the acoustic emission released data in the

form of events, counts, energy, cumulative counts. As shown in Figs. 5 to 8, the

counts are recorded with calibrated weld position of the sample in the range of 60

mm - 80 mm to evaluate the events as of weld zone region.

As soon as the microcrack/crack initiated, the counts and energy release

condition shows a sudden rise in the measurement of recorded AE events data.

Once when the sample began with plastic deformation, there was no further AE

events recorded, the events suddenly started to reduce and disappear. The variation

includes the arrival of the events on the sensors with the event generation. The

amplitude and frequency were recorded, in which, waveform and FFT analysed

spectra are shown in Figs. 9 and 10.

Fig. 5. Variation of event vs. position with respect to

AE data on defects free weld sample.

Fig. 6. Variation of count vs. time (s) with respect to

AE data on defects free weld sample.



Fig. 7. Variation of energy vs. time (s) with respect

to AE data on defects free weld sample.

Fig. 8. Variation of count vs. amplitude with respect

to AE data on defects free weld sample.

Fig. 9. Raw AE data during loading with

frequency (micro sec) in sensors 1, 2.



Fig. 10. FFT analysed data with amplitude vs. frequency.

In the case of weld joint with porosity, weld defect is subjected to the loading

condition, the AE events start initiated much earlier and the intensity-released

energy is higher than the sample without weld defects. This indicates the

mechanism of the stress release due to the stored energy release due to the applied

loading has contributed towards higher measured data in all forms. The events

recorded under the porosity weld defect are shown in Figs. 11 to 14.

Fig. 11. Variation of events vs. x-position with respect

to AE data on porosity defects weld sample.

Fig. 12. Variation of count vs. time (s) with respect

to AE data on Porosity defects weld sample.







The events, counts, energy and amplitude data has shown significant

enhancement compared to weld sample without defects as shown in Figs. 5 to 8.

This phenomenon is analogous to the release of stress wave locally in the welded

region during the loaded condition in the form of acoustic emission frequencies

recorded by sensors at each end attached to the weld sample. The stress

concentration released under the dynamic loading has shown significant influence

on the higher counts, amplitude, the energy released during the crack initiation and

enhanced stress energy waves measured in AE system data. The crack initiation

and plastic deformation zone in the welded zone have shown significant events

enhancement in measured AE data with porosity defect sample data compared to

the without defect sample data. This corresponds to the role of weld defects causing

stress release during the dynamic loading into the localised welded parts, which

have scope towards the failure of components due to the mechanical events in

service conditions.

As shown in Figs. 15 and 16 are the characteristic waveforms recorded during

the applied load and the FFT analysed data, which reveals the burst emission of AE

data events in both the channels received are having 280 kHz and 290 kHz. The

gradual increase in amplitude is attributed towards the crack initiation with initial



loading and final fractures in the weld zone after the plastic deformation takes place

when compared to Figs. 9 and 10.


frequency (micro-sec) in sensors 1, 2.


Similarly, in case of the sample with slag inclusion defects, the load test

conditions have revealed significant enhancement and rise in the AE recorded data



in terms of events, counts, energy and amplitude compared to sample without

defects. The measured data, (Figs. 17 to 20) is shown for the slag inclusion sample.

The data enhancement contribution has revealed the stress release in terms of the

acoustic frequencies, which are more prominent than the good weld sample (defects

free). The parameters events, energy and amplitude show a similar trend of rising

in both the weld defect samples under the dynamic load condition in AE recorded

data. However, the slag inclusion defect sample has shown relatively low events

data compared with porosity condition defect sample. This further confirms to the

generation of the event during the crack initiation and propagation process has

proclamation more stress energy during the loading and is reflected in the where

the burst emissions occur in the form of acoustic emission events. As shown in

Figs. 21 and 22, the measured waveform recorded during the AE data collected

with burst mode emissions at different frequencies in both the sensors attached

during loading condition. The lower frequency recorded as 60 kHz and 85 kHz are

observed in the peak data of the FFT analysed waveform. This further count to the

lower emission caused compared with porosity weld defect sample.

Fig. 17. Variation of events vs. X-position with respect

to AE data on Slag defects weld sample.

Fig. 18. Variation of count vs. time (s) with respect

to AE data on slag defects weld sample.








frequency (micro sec) in sensors 1, 2.




The recorded AE in continuous mode and burst mode conditions due to the

applied dynamic load and the events recorded during the initial stage, crack

initiation and crack propagation (fracture mode) and release of stress concentration

due to the weld defect samples are shown in Figs. 23 to 25. The waveform recorded

initial condition shows that the dominant frequency at 60 kHz in FFT analysed the

signal. In the case of crack initiated during the dynamic load application stage prior

to the plastic deformation, the released energy in the form of recorded frequency

was shown burst mode emission characteristics in Fig. 24 shown dominant peak

frequencies at 40 kHz and 130 kHz. The crack propagation region where the

complete fracture initiated, the burst emissions with peak amplitudes of 50 kHz and

120 kHz frequencies. This reveals the contribution of the acoustic emission events,

which are due to the defects caused a failure like cracks of the structure under the

dynamic loading conditions.

The present study postures the capability of the acoustic emission technique as

a potential diagnostic in preventing the failures due to the excess load to the

mechanical structures and integrity aspects. This detection method can be applied

for structural health monitoring technique towards the examination of heavy

structural materials during the in-service condition of the structural steel welded

components. Acoustic emission system can be used as a potential diagnostic

technique to monitor the failure events on structural integrity during the mechanical

operation loads.



Fig. 23. AE data and FFT waveform analysed

during the initial load condition.

Fig. 24. AE data and FFT waveform analysed during crack initiation.



Fig. 25. AE data and FFT waveform analysed

during the crack propagation under stress condition.

4. Conclusion

The present study reports the detailed examinations carried under the dynamic load

applied to weld structures with different weld defects.

The AE system can be exploited to demonstrate the structural stability

condition of the weld joint steel structure during the dynamic load

conditions been studied by setting up the special mechanical cell with

localized load supply system with AE system integration towards

demonstration of the event registration during failure conditions in the

structural integrity of the components.

The good welded joint under dynamic load with special designed

mechanical jig system has shown the AE events in the form of events,

counts, energy and amplitude with respect to the weld position during the

crack initiation and propagation.

The weld sample with porosity condition has shown the increase in the AE

measured parameters like counts, energy and amplitude with position

contribution from the excess stress energy release (burst modes) due to the

weld defect region.

The sample with slag inclusion has shown the AE measured data parameters

relatively lower than the porosity sample but enhanced than the good weld

sample. This further confirms that the stress release under dynamic load on the

inclusion weld defect samples shows less amount of stress released during the

load applied condition.



The tailored AE measurements data events can be logically interpreted and

inspection can be upheld for the critical component structures and welded joint

regions where the critical failure is anticipated or prevention is necessary. The

monitoring of AE parameters of defect-free and weld defect (porosity) samples

adequately demonstrates that AE technique is a potential for online health integrity

monitoring of premier and critical installations like nuclear reactors, pressure

vessels and chemical plants under service loads.

Acknowledgements

One of the Authors, S. V. Ranganayakulu, is grateful to Board of Research in

Fusion Science and Technology of Department of Atomic Energy (Project No.

NFP-MAT-F12-01) for sanction of grant-in-aid to carry out this word as part of the

execution of the academic research project.

Nomenclatures

K Frequency FFT analysed data in kilo 1000 (Fig. 16)

T Time in seconds (s)

Abbreviations

AE Acoustic Emission

AISI American Iron and Steel Institute

ASTM American Society for Testing Material

FFT Fast Fourier Transform

LLT Linear Location Technique

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