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International Journal of Mechanical Engineering and Technology (IJMET) Volume 1, Issue 2007, Jan–Dec 2007, pp. 22–34

Available online at

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

© IAEME Publication

NON DESTRUCTIVE EVALUATION OF

AUSTENITIC STAINLESS STEEL

WELDMENTS BY ULTRASONIC TESTING

METHODS APPLYING ADVANCED SIGNAL

ANALYSIS APPROACHES

Dr. S. Ravichandran

CEO & Chief Scientist, Trimentus Technologies

T. Nagar, Chennai, India

Email: drravis@trimentus.com

1.0 INTRODUCTION

Many grades of austenitic stainless steels are extensively used in nuclear, space, chemi-

cal, and petrochemical industries. Austenitic stainless steel weldments used in these demand-

ing service conditions, must be subjected to stringent testing / non - destructive evaluation for

assuring desire quality. Early detection of defects in these weldments, before they grow are

detected by conventional ultrasonic and radiography testing methods. Radiographic testing of-

these weldments has limitations posed by weld geometry and accessibility of both sides of the

weld for inspection. The attenuation of ultrasound energy is due to the dendritic textured /

columnar microstructure present in the weld and the consequent scattering in the grain

boundaries. This result in a decrease in the signal to noice (S / N) ratio and as the thickness of

the weldment increases, the reduction in the signal to noise ratio is appreciable.

During conventional ultrasonic testing of austenitic stainless steel weldments,

indications are obtained during ultrasonic testing, resulting in poor sensitivity and reliability

being a versatile material, austenitic stainless Steel represents about 60 percent of all stainless

steels used, in industrial equipment, structures and pressure vessels, and are. used in a wide

range of temperatures. Austenitic stainless steel welds are employed for their good

mechanical properties at elevated temperatures, excellent corrosion resistance, ease of

fabrication with good weldability, fracture toughness etc. Scattering of ultrasonic

waves, in the grown boundaries during ultrasonic test, they result in reduction in the

signal to noise ratio, hence poor sensitivity of defect detection in its ultrasonic test.

Incomplete penetration, porosity, slag inclusions and line defects (cracks) are some typi-

cal defects encountered in these weldments. The origin of these defects can be traced to

welding parameters such as welding speed, motion of the weld pool, torch cross angle,

oscillation etc.

It is desirable to detect cracks having depth below 5 percent of weld thickness in

austenitic stainless weldments. However the texture exhibited by the welds of these materials

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along with the limitations of conventional ultrasonic methods has made the detection of these

defects a very difficult task.

The early detection of small defects before they grow to a level detectable by conven-

tional ultrasonic methods will help to monitor the cracks during in-service inspection, enable

remedial measured to be taken to reduce the downtime of the equipment / plant, minimize

repair cost, and enable the predication and extension of the remaining life of the plant. The

minute and small cracks are not generally expected to grow to the larger size in a

ductilematerial like austenitic stainless steel. However, defects in critical applications may

grow under the influence of its service condition, residual stresses and imposed fatigue

loading, etc. the reliable detection of large natural defects in these weldments is to be

achieved to prevent their failure while in service.

To achieve these goals, it become necessary to explore, develop and practice the tech-

niques to overcome the limitations posed by ultrasonic testing due to the dendritic textured

microstructure. In order to realise these objectives, advanced signal analysis methods were

adopted and applied successfully.

2.1 NEW METHODS IN THE NDE ON A USTENITIC STAINLESS STEEL

WELDMENTS

2. LI Signed Analysis As we know, high scattering of ultrasonic waves leading to increased attenuation, results

in a low signal to noise ratio in austenitic stainless steel weldment. Ultrasonic inspection of

austenitic stainless steel weldments poses a number of problems, suggesting ultrasonic signal

analysis as a useful possibility and strategy for achieving success. These signal analysis meth-

ods can characterise defects and increase sensitivity of defect detection. Even when special

transducers, which aid ultrasonic inspection are not used, and when conventional ultrasonic

transducers are used, signal analysis methods can improve the defect detection sensitivity.

2.1.2. Signal processing approach

The term signal came into general use to denote 'a sign or notice, perceptible by sight

or hearing, given especially for the purpose of conveying warning, direction or information'.

Later as electronics and communications grew to what they are today, the word 'signal'

included almost any physical variable of interest and paved w ay to extend the techniques of

signal analysis and processing to other areas of enquiry.

Today it is very difficult to name an area in the frontiers of sciences where signal

processing is not used. Apart from areas such as communications etc., signal analysis is used

in medicine geology, seminology, mechanical engineering, zoology, music and norr-

destructive testing.

To detect and characterize defects in textured weldments using signal analysis, it is es-

sential to capture the signal using the appropriate transducer. The captured signal can be

analyzed either as it is (i.e, analog signal), or it can be digitized and the digital signal can be

processed.

The digital signals are preferred because of the ease with which they can be processed

using digital computers.

2.3. BASIC METHODS OF SIGNAL PROCESSING

2.3.1. The Principle

Due to the presence of noise, the signals from the smaller defects are masked. Conven-

tional pulse - echo technique, in the absence of modern digital signal analysis, relying mainly

on the measurement of how much the defect signal amplitude is, above the mean noise / grass

level, in a flaw detector, is not able to resolve signals from noise. But the basic 'information'

about the defects is still present in the signals. Analysis is carried out in both time and fre-

quency domains, and the following parameters, which are due to the phase and amplitude dis-

tributions of frequency components, are extracted from these signals.

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a) demodulated autocorrelation function (DMAC)

b) Scalar mean peak power (SMPP) and

c) normalized mean deviation vector (NMDV).

Method (a) is basically a pattern recognition approach in time domain, showing the pe-

riodic nature of the signal. Methods (b) and (c) use cluster analysis principles, by which a set

of objects is divided into several groups or clusters so that objects within the same cluster are

more similar to each other than those in different groups. Cluster analysis methods use prop-

erties of the signals in the frequency domain. The form of differences in relative phases and

amplitude variations of the frequency components provide information to resolve defect sig-

nals from noise, which is not possible in the conventional method.

2.3.2. Pattern recognition approach

Pattern recognition methods aim at extracting and interpreting features from an existing

or generated pattern in order to detect and characterize the source of the pattern.

In this method, the log of the square of the autocorrelation function, ((f>T) forms

different patterns for different defects and noises (<f)T). The value of (<f)T) is highly

fluctuating, making it necessary to smoothen it by extracting the pattern envelope. The

envelope of a pattern is extracted by successive maximum technique. In this technique, every

(<|)T) value, say Yn, of the pattern is replaced by the maximum among Yn,

Yn+1> Yn+2 ........... Yn+9,

thereby smoothening the entire pattern. The resulting pattern is called the Demodulated

Autocorrelogram (DMAC) pattern, which is the power (in dB) vs delay (in ^ts). Plot of the

demodulated autocorrelation function, is obtained by taking the log of the square of the

autocorrelation function of any signal. The demodulated autocorrelograms form different

patterns for signals from defect free regions of the material. Visual examination of the

patterns and pattern recognition principles applied to these patterns are able to differentiate

and characterize noise and defect signals.

The success of this powerful technique lies in the ability to interpret the resulting

envelope pattern to characterize the defect. Using the above principle, on the austenitic defect

of 5% thickness has been detected and characterized. The DMAC pattern obtained on the

above said defect is given in Fig. la and Fig. lb.

From the above discussion, it can be seen that non-destructive evaluation of austenitic

stainless*weldments posses practical difficulties due to their dendritic textured micro

structures. It is difficult to evaluate thick austenitic stainless steel weldments with

conventional ultra-

Fig 1. Demodulated Auto Correlogram patterns

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sonic pulse echo technique due to the problem of acoustic anisotropy. This problem is

more as the thickness -of the austenitic stainless steel weldments increases. The radiographic

testing does not yield good results due to the limitations of thickness and orientation of

defects. Signal analysis of the echoes / signals received from commercially available probes

can yield important information and could be used in the ultrasonic testing. Detection of fine

defects in 14.0mm thick austenitic stainless steel welds has been done successfully, detecting

artificial defects (4% and 5% thickness notches) using pattern recognition method. There are

no published literatures available in detecting artificial and natural defects in the austenitic

stainless steel weld thickness exceeding 17.0mm with these signalanalysis techniques. The

present work involves detection of artificial defects on 25 mm thick austenitic stainless steel

weldment using above methods.

3.0 EXPERIMENTAL PROCEDURES

3.1 MATERIAL Austenitic Stainless steel

AISI type 304 austenitic stainless steel weld plates were used for this study. Typical

chemical composition of austenitic stainless steel is shown in Table 1. The plates were ascer-

tained to be defect free by employing ultrasonic testing before welding.

TABLE- 1 Chemical Composition of AISI Type 304 Austenitic Stainless Steel

Element By % weight

Carbon 0.08

Chromium 19.0

Nickel 10.2

Nitrogen 0.03

Manganese 2.0

Silicon 1.0

Phosphorous 0.035

Sulphur 0.03

Iron balance

3.2 WELD PREPARATION

3.2.1. AISI Type 304 austenitic stainless steel weldment

Austenitic stainless steel plates having thicknesses 25.0 mm was fabricated for this study. The

welding process used for the fabrication of these plates is shielded metal arc welding

(SMAW). Single - 'V (75°) butt welding was carried out as per ASME Boiler and pressure

vessel code. After the welding process, the plates were machined. This weldment was chosen

for incorporating artificial, electric discharge machining (EDM) fabricated defects (notches)

in it, simulating the natural defects (line defects). Artificial defects such as notches having

depth equal to 5, 4. 3, 2 and 1 per cent of plate thickness (parallel to the weld direction).

These artificial defects are referred to as 'fine defects" in this thesis. These defects are

depicted in Fig. 2 and Table - 2.

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Fig. 2 : Schematic Diagram Showing Locations of Artificial Defects in Austenitic Stainless Steel Weldment

of 25 mm Thickness

TABLE - 2 Size of EDM Fabricated defects in 25.0 mm Thick Austenitic Stainless Steel weldment

Type of Defect Depth

in mm

Width

in mm

length

5% Notch 5% of T 2.50 12.5

4% Notch 4% of T 2.00 12.5

3% Notch 3% of T 1.50 12.5

2% Notch 2% of T 1.00 12.5

1% Notch 1% of T 0.50 12.5

T - Thickness of the weld = 25.0 mm.

These notches were made on the top midsection of the weld, along the axis of the weld

(longitudinal notches). The details of these defects are shown in Table 2.

4.0 RADIOGRAPHY EXAMINATION The radiographic examination of stainless steel weldment was performed with X - rays.

These results were used to arrive at a basis for examination of the welds of conventional

ultrasonic testing and signal processing.

5.0. ULTRASONIC TESTING

The following experimental procedures described were adopted for the ultrasonic test-

ing and signal acquisition in both the mild steel and austenitic stainless steel weld plates.

5.1 PROBES USED

The shear wave probe with a frequency of 4 MHz can be used on 12.5 mm plate, as this

frequency was used in the experiment done earlier to detect fine defects. However, for higher

thickness weldments of austenitic stainless steel, above 17.5 mm onwards, a lower frequency

of' 2 MHz was used to limit the scattering of ultrasound. A shear wave probe (45°, 2 MHz,

WK-45-P2) was used for testing of carbon steel weldment and dismilar metal weldment.

Shear wave probes were used since these probes may be employed with advantage in

the ultrasonic examination of austenitic stainless steel weldments of thicknes 10-47 mm as the

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beam do not undergo mode conversion. The band width of the shear wave probes used is

approximately, ± 0.2 MHz in 2 MHz probes and ± 0.4 MHz in 4 MHz probes.

(45°) angle was chosen for this study because, for this angle, the beam deviation (angle

between the propogation direction and the normal to the wave front) is minimum.

5.2. TESTING PROCEDURE

The following procedure was adopted for testing of all the above weldments.

( a ) Conventional Ultrasonic Testing:

Detection sensitivity of conventional pulse - echo technique was studied by finding the

difference in the percent full scale height (FSH) of echoes / indications from defective and

defect free regions in the weldment at a constant gain setting. For this purpose signals were

studied using a flaw detector (Ecograph - 1030) at full skip distance on the reinforcement

(top) side (Fig. .3).

Fig. 3: Location of Transduser at full skip distance from the centreling of the

weldment for ultrasonic testing

The defect locations already detected by radiography testing were considered as the basic

information for performing conventional ultrasonic testing. The shear wave probe with 4MHz

and 45° was used for testing. The experimental setup is shown in Fig. 4.

( b ) Ultrasonic Testing Using - Signal Analysis:

For signal analysis purposes, the same Echograph - 1030 was used for exciting the

probe, amplifying and gating the received signals.

During testing, the probe was kept on reinforcement side of the weldment. Signals

were acquired by keeping the probes on either side if the centre line of the weldments.

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Fig. 4: Block diagram for the experimental set-up

In the case of notches, testing was carried out by keeping the probe at full skip distance

(from the weld centre line). For every defect, signals were acquired, by bringing various

portions of defect under the purview of the test. Three noise signals were acquired, per plate,

in an attempt to generate reasonable parameters, from different defect - free portions of the

weldments.

The signals were amplified with a constant gain setting in the ultrasonic flaw detector.

The gain was set in such a way that the mean percent full scale height of noise signals was

about 20. The amplified signal was gated so as to obtain signal of interest and digitized at a

sampling rate of 108 samples per samples per second (i.e., with a sampling interval of 10

nanoseconds) for analysis. A suitable constant threshold and a pre - trigger level (-200 points,

equivalent to -2.0 fis) were set during data acquisition. The gate delay and gate width of the

Echograph were adjusted so as to capture the time domain signal from different locations on

either side of the peak of the overall time signal. Signal analysis was carried out with the

digitised data (of time duration 20.48 us; data length 2048 points) using a signal analyzer

(Iwatsu signal Analyser SM2100B and Iwatsu Digital Memory DM 902). The digitised time

domain data were stored in the floppy drive of the SM 2100B Signal Analyser, for further

processing with demodulated autocorrelation function analysis. Signal processing was carried

to confirm the large size defects detected by conventional ultrasonic testing and to detect the

defects undetected by ultrasonic testing.

5.5. ADVANCED SIGNAL ANALYSIS METHOD FOR THE NON- DESTRUCTIVE

EVALUATION FOR WELDS Extraction of information relevant to detection and characterization of a defect from a

series of discrete data (Digitized Time Domain Signals) often calls for application of

advanced Signal Analysis Procedures and methods. Very rarely, time - domain data (sampled

data) offer the necessary information without any processing and analysis. More often than

not, to extract specific information from the signal, it is necessary to study the signal in

various domains, other than time. Both time and frequency domain analysis approaches are

used in this study.

6.0. RESULTS AND DISCUSSION

6.1. RESULTS OF RADIOGRAPHIC TESTING. The radiograph of austenitic stainless steel weldment of 25.0 mm thick did not reveal notch of

1%, 2% and 3% thickness. However 4% and 5% thickness notches were detected. (Fig. 5.)

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6.2. RESULTS OF CONVENTIONAL ULTRASONIC TESTING

This testing results are shown in Table 3 The difficulty encountered in the conventional

ultrasonic testing is that the present of noise throughout the weldment produces a defect like

indications / echoes on the CRT Screen

Fig. 5: Radiograph of 25mm thick austenitic stainless steel weldment

TABLE - 3

Results of conventional ultrasonic testing of artificial defects in 25.0mm Austenitic S.No. Defect Amplitude in the flaw detector (Average value) Gain

Setting

1. Noise 25% 20 dB

2. 1% Notch 19% 20 dB

3. 2% Notch 20% 20 dB

4. 3% Notch 25% 20 dB

5. 4% Notch 33% 20 dB

6. 5% Notch 37% 20 dB

with the signal from the defect below a certain size, which could have been detected

otherwise. Therefore, reasonable results are difficult to obtain by conventional pulse - echo

ultrasonic testing, because of low signal to noise ratio.

6.3. RESULTS OF ADVANCED SIGNAL ANALYSIS APPROACHES Signal analysis approach was adopted for defect detection in these weldments, using

the total spectral energy and the pattern recognition approach. The patterns, as described were

obtained using the autocorrelation function, for all the noise and defect signals.

6.3.1. Results of the total spectral energy (TSE) approach The results obtained using this approach are shown in Table 4. The TSE values of defect

signals are at least twice in magnitude as compared to those due to noise signals. In thicker

weldments, TSE values of noise signals would be larger due to larger dendritic noise present

in the weld. Hence, it can be concluded that for large defects, TSE is a good criterion to

detect defects. However, for small defects, the pattern recognition approach is recommended.

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6.3.2. Pattern Recognition Approach Demodulated autocorrelogram patterns are shown in figures Fig. 6 (a to f) are patterns

obtained using signals from the artificial - defects on stainless steel plate of 25.0 mm. The

following observations were made : Figure 6 a corresponds to a typical noise pattern. From

the patterns of noise, 1 %, 2% and 3% other notches, it is observed that the noise waveform

pattern contains more number of narrow lobes (seven full lobes) within a time window of ±

2.5 ps, whereas the lobes are wider, i.e., less in number (three full and two fractional lobes)

for the same time interval in the patterns obtained from the detect waveforms, confirming the

published results. It can be observed that the DMAC pattern for the 1% defect is very similar

to that of a noise waveform.

7.0. CONCLUSION

i) From the above work carried out. the following conclusions are arrived at.

Austenitic stainless steel plate weldment of 25mm thick, do not reveal the defects

lesser than 4% thickness in radiographic testing.

TABLE - 4 Total Spectral Energy

S.No. Weld Defect Type Total Spectral Energy

(TSE)

1.

Austenitic Stainless Steel

25 mm thickness

Noise 1870

1% Notch 2100

2% Notch 2450

3% Notch 2700

4% Notch 2900

5% Notch 3107

Fig 6(a) Dmac Pattern on 25mm Austenitic Stainless Steel Weldment

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Fig 6(b) Dmac Pattern on 25mm Austenitic Stainless Steel Weldment

Fig 6(c) Dmac Pattern on 25mm Austenitic Stainless Steel Weldment

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Fig 6(d) Dmac Pattern on 25mm Austenitic Stainless Steel Weldment

Fig 6(e) Dmac Pattern on 25mm Austenitic Stainless Steel Weldment

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Fig 6(f) Dmac Pattern on 25mm Austenitic Stainless Steel Weldment

ii) The conventional ultrasonic testing, fine defects (equivalent to notches having

depth less than 4% of thickness) in austenitic stainless steel weldments cannot be detected due

to low signal to noise ratio. The larger sized defects can be detected using conventional ultra-

sonic testing in austenitic stainless steel weldments.

iii) With the application of advanced signal analysis method using total spectral energy

and demodulated autocorelogram pattern recognition studies, on austenitic stainless steel

weldments, higher sensitivity can be achieved. The presence of more number (five or more)

of lobes is the characteristic of a pattern obtained from a noise waveform. For a

corresponding defect pattern, presence of fewer lobes is the characteristic. All the fine and

large sized defects in this weldment can be distinguished from noise signals, by applying the

above criterion.

Larger width for the central lobe and larger difference between the peaks of the central

and adjacent lobes are the features of a pattern obtained from the signal from the signal hole

in austenitic stainless less weldment.

By applying the criteria of the pattern recognition method, it is possible to detect the

defects in austenitic stainless steel weldments. It is possible to detect the defects, to meet the

intent of ASME XI, Boiler pressure vessel code, Rules for inservice inspection of nuclear

power plant components in the thicker weldments of austenitic stainless steel material.

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AUTHOR PROFILE

Dr S. Ravichandran is a Software Quality Professional. He is also a Six Sigma Master Black Belt

with academic qualifications ME ,MBA ,Ph.D .He has carried out software quality appraisals,

consultancy and training services in India and over 20 countries such as China, Brazil, Singapore,

Malaysia, US, Europe, Middle East etc He is Presently the Chairman, CEO and Chief Scientist of

Trimentus Technologies, Chennai & US. He also well known expert in engineering quality including

NDT techniques.