ToFD

TOFD IMAGING

Peer-reviewed

Ultrasonic TOFD flaw sizing and imaging in thin plates using embedded signal identification technique (ESIT) G Baskaran, Krishnan Balasubramaniam, C V Krishnamurthy and C Lakshmana Rao

The ultrasonic Time-of-Flight Diffraction (TOFD) technique is a well developed technique for sizing defects in thick sections (thickness >10 mm). Attempt has been made here to extend this technique for thin sections (6-10 mm). An automated defect sizing algorithm using the Embedded Signal Identification Technique (ESIT) was developed for separating partially superimposed signals often encountered in thin sections and the results were compared with the manual sizing method. Both EDM notches and more realistic fatigue cracks in thin section were used to evaluate the proposed technique.

1. Introduction Conventional ultrasonic technique uses the pulse transit time to locate the flaw and the amplitude to size the flaw. For accurate flaw sizing, the amplitude as a parameter may not always be sufficient, since the amplitude of the reflected pulse is influenced by factors other than the size of the reflector such as the surface roughness, transparency and orientation of the defect. When an ultrasonic wave encounters a crack-like-defect, it undergoes reflection, transmission and diffraction. According to Huygen’s principle, the crack tips act like secondary point sources re-radiating energy over wider angles than what is possible under reflection and transmission. The diffracted component can therefore be singled out spatially using suitably positioned ultrasonic transducers. The defect sizing method based on the timeof-flight of these diffracted waves at the crack edges is called the Time-of-Flight Diffraction (TOFD) method[1]. The basic principle and probe set-up of TOFD technique is shown in Figure 1. The B-Scan image components thus obtained are shown in Figure 2. Two longitudinal wave broad-beam probes of the same angle, preferably 55-70° (one is transmitter and the other is receiver), are placed on the sample in a pitch-catch like configuration as shown in Figure 1. Longitudinal waves are preferred since the diffraction coefficient of the longitudinal waves is higher than that of the shear wave. The distance between probes can be calculated based on the thickness of the structure. Four different types of signals (labelled 1 to 4 in Figure 1) are used in the TOFD imaging[1]. Signal 1 arises from the wave that travels directly from the transmitter to the receiver along the surface. This wave, referred to as the lateral wave, has also been called subsurface or creeping longitudinal or critically refracted longitudinal or head wave in the literature. It has the shortest time of travel because it travels the G Baskaran, C V Krishnamurthy and C Lakshmana Rao, Department of Applied Mechanics, Centre for Nondestructive Evaluation, Indian Institute of Technology, Madras, Chennai, India. Krishnan Balasubramaniam, Department of Mechanical Engineering, Centre for Nondestructive Evaluation, Indian Institute of Technology, Madras, Chennai, India. Contact: E-mail: [email protected]

Insight Vol 46 No 9 September 2004

Paper submitted 04 May 2004

Figure 1. Schematic of the TOFD technique. S is the probe separation, L is the defect depth and D is the defect size. Labels 1, 2, 3 and 4 denote the lateral wave, the echo from the top of the defect, the echo from the bottom of the defect and the backwall echo respectively

shortest distance with a speed that is close to the longitudinal wave speed in the structure. Signal 2 arises from wave diffracted by the top edge of the defect and travels at longitudinal wave speed. Signal 3 arises from the wave diffracted by the bottom edge of the defect and also travels at longitudinal wave speed. Signal 4 arises from the mode converted longitudinal wave reflected from the bottom surface. The strong lateral (signal 1) and backwall (signal 4) signals are used to time-gate the region of interest. The weak diffracted echoes are expected to appear within this region. By measuring the transit time between the diffracted echoes from the top and bottom of the crack, the defect depth and defect size can be computed[1].

Figure 2. TOFD B-scan image components for a 3.25 mm vertical defect in a 10 mm aluminium sample

2. Background TOFD was originally developed as a method of accurately sizing and monitoring the through-wall extent of welding defects and inservice flaws, primarily in steel components. So far, this technique has been applied successfully to thick sections. Several papers have reported this technique for the inspection of ‘thick’ sections. The details of the various applications of TOFD are explained in many publications[2-4]. Even though the basic principle, the experimental set-up, and the interpretation may be similar for both thin and thick sections, experimental parameter selection and signal identification requires more attention in the case of thin sections. The details of implication of the ASME XI and other codes for the TOFD

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technique are given in[4] for thickness greater than 10 mm. These codes also explain the resolution and near-surface problems in this technique. The theory of elastic wave diffraction and selection of probe angles for obtaining maximum diffraction coefficients are explained by Ogilvy and Temple[5]. The details of various signal processing technique used to improve the interpretation of ultrasonic data are given by Silk[6]. The position and size of the flaw cannot be directly read from the images when there is an offset between the transmitter and receiver in case of non-horizontal flaws and also use of broad beam transducers produces broad defect curve shapes. These problems can be solved by the SAFT algorithm explained in the literature[6, 7]. In practical situations, it becomes necessary to size the defects in ‘thin’ sections, for example detecting weld fatigue cracks in a rocket motor outer casing of 6-8 mm thickness maraging steel, that is attempted here.

3. Inspection of thin sections (< 10 mm) Some of the difficulties with TOFD inspection of thin sections can be listed as below: q The number of mode converted signals reaching the receiver transducer increases with decreasing thickness of the specimen. q As the specimen thickness decreases the spacing between lateral wave and backwall echo decreases. The crack tip echo (due to L-wave) always lies in the region between lateral and backwall echoes and hence temporal resolution plays a determining role when dealing with thin section. The signals overlap making TOF calculations difficult. q As the thickness of the sample reduces, the critical flaw size also consequently decreases. This leads to the reduction in the separation between the two tip diffracted echoes wish normally

overlap of the tip diffracted signals, leading to difficulties in TOF calculations. Figure 3 shows the comparison of spacing between lateral wave and backwall echo for 20 mm and 10 mm thickness aluminium samples for 60° L-wave probes, 5 MHz frequency and 25 mm distance between probes. The problem of inspection of thin sections can be tackled by paying attention to the following details: q Optimising experimental parameters (distance between probes, angle of the probe and frequency); q Reducing the distance between probes (transmitter and receiver), which causes the number of signals reaching the receiver to be reduced. The probes designed for this work have smaller probe separations (24-27 mm); q Using an alternate signal processing techniques, such as Embedded Signal Identification Technique (ESIT) discussed here. This ESIT technique is used to measure TOF from superimposed signals; q By using higher frequency (>5 MHz) the resolution of the B-scan image can be increased. This may be acceptable since attenuation is less of a problem in thin samples.

4. Specimen preparation Two sets of samples were prepared for this study of investigation of TOFD for thin sections: Calibration block with EDM notches Aluminium and mild steel specimens of 10 mm thickness with various configurations of EDM notches (FBH, SDH, vertical slots, inclined slots) were prepared. The block diagram of calibration sample with various EDM notches is shown in Figure 4. The details of the vertical slots are shown in Table 1.

Figure 4. Sketch of various EDM notches in the calibration block. t is the thickness of the specimen of width 100 mm and 1 to 24 are the defect numbers (a)

Maraging steel sample with fatigue crack Three maraging steel weld samples with realistic fatigue cracks in the weld region, with different crack depths, were prepared. A through-width V-notch was made to initiate the crack. The dimensions of the specimen and the crack initiating V-notches are provided in Figure 5. The sample was fatigued in a standard servo hydraulic testing machine. The crack initiation was monitored using the acoustic emission technique. Once the crack initiated, the crack length was controlled by controlling the load and number of cycles in Table 1. The details of vertical EDM notches, size and depth inside the calibration blocks. All notches are 0.5 mm width and 8 mm deep

(b) Figure 3. Comparison of spacing between lateral and backwall echoes for (a) 10 mm and (b) 20 mm thickness aluminium samples using 60° probe angle, 5 MHz frequency and 26 mm probe separation

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DEFECT NUMBER

TYPE

LENGTH (t=thickness)

LOCATION (t=thickness)

D1

VERTICAL SLOT

50% of t

CENTER AT 50% of t

D2

VERTICAL SLOT

25% of t

CENTER AT 50% of t

D3

VERTICAL SLOT

12.5% of t

CENTER AT 50% of t

D4

VERTICAL SLOT

6.25% of t

CENTER AT 50% of t

Insight Vol 46 No 9 September 2004

Figure 5. Sketch of (a) side and (b) top view of fatigue samples with varying notch sizes (X and Y). All dimensions in mm

the experiments. The sample was then ground to remove the crack initiator V-notch. This resulted in a surface-breaking realistic crack inside the material (except in one sample, where the effect of weld bead and crack initiator V-notch was studied). The details of the V-notch dimensions and fatigue parameters are shown in Table 2. Table 2. Fatigue testing details Specimen number

Specimen thickness before (after) machining

V-notch dimensions

Fatigue load maximum (loading ratio 0.2)

Number of cycles 103

% Controlled Crack depth

Estimated crack size

1

8.5 (6.43) mm

2 mm width 2 mm depth

4.5 tons

750

More than 50% of the thickness

4.52 mm

2

8.58 (7.2) mm

1.5 mm width 1.5 mm depth

4.0 tons

600

Around 50% of the thickness

3.2 mm

3

8.53 (7.5) mm

1 mm width 1 mm depth

3.0 tons

520

Less than 50% of the thickness

1.2 mm

5. Development of TOFD system An in-house TOFD system was developed for this research work. The photograph of the system and its components are shown in Figure 6. This TOFD system essentially consists of: q The spring-loaded transducer holder to improve the consistency and robustness during automated scanning; q Pulser/receiver (Panametrics 5058PR/5900PR); q A multi-purpose X-Y scanner controlled by stepper motors and integrated with the motion control hardware for data acquisition; q A 100 MHz analogue/digital converter.

Embedded Signal Identification Technique (ESIT) that is described in the following paragraphs. To size the flaw accurately it is necessary to measure accurately the time of arrival between echoes. The ESIT helps in the identification of the superimposed signals into different wave component reaching the receiver thus making it possible to measure the time of arrival accurately. When two signals are partially or fully superimposed, the resultant signal shape is altered. This is visible in both the A-scan as well as in the frequency spectrum. From the A-scan it is difficult to extract the time difference between the two superimposed signals, since it is difficult to identify identical points in the two signals from which TOF can be derived. Hence an ESIT technique is discussed here to extract the time difference. Consider the typical time signal representation:

x(t) = Ae

-at

cos(wt) .............................(1)

The Hilbert transform of x(t) is given by:

x H (t) = - Ae-at sin(wt) ............................(2) Where ‘A’ is the amplitude, w = 2pf and ‘a’ is the decay factor of any RF signal. The analytic signal then can be represented as:

x A (t) = x(t) + x H (t) .............................(3) -at

= Ae (cos wt - i sin w t) By taking modulus and then the natural logarithm of the analytic signal given by:

x A (t) = Ae-at ....................................(4)

ln x A (t) = -at + ln A ............................(5) Which has the form of a line with slope -µ. Hence, in a composite signal consisting of two signals having different as, the point of intercept provides a point of reference in the form of a minima (valley). Thus, the time constant of the system can be extracted by plotting ln|xA(t)| vs time and finding the minima in this plot using standard peak/valley detection algorithms. Figure 7 shows a typical A-scan over an EDM notch and the corresponding plot of logarithm of analytical signal as a function of time for the automatic defect sizing algorithm. This algorithm consists of four stages. In the first stage the B-scan image is processed using low pass Butterworth band-pass filter to remove any noise outside the intended bandwidth of the experiment. In the

� � Figure 6. TOFD robotic system with the X-Y scanner and the spring loaded dual probe used for data collection

� signal identification technique 6. Embedded � (ESIT) Defect sizing from the B-Scan image can be done by manually positioning the cursor over the tips of the defect echo. The reliability of the manual defect sizing depends on the image quality.�To position the cursor exactly at the tip requires high contrast images that may not be feasible for thin structures due to super position of signals. � An automatic defect sizing algorithm was developed to improve the reliability of sizing. In this software from the B-Scan image, at the point of interest, the A-scan is extracted and processed by

Insight Vol 46 No 9 September 2004

Figure 7. Extraction of time information from superimposed signals. (a) A-scan from a typical EDM notch (0.8 mm size, using 60° probe angle, 5 MHz probe frequency and 24 mm distance between probes) and (b) the corresponding echo separated signals

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second stage, a single A-scan is extracted at the cursor location. In the third stage the extracted signal is processed using the ESIT. In the fourth stage the echo separated signal is processed to get valleys (minima) using standard valley detection algorithms. The software Graphical User Interface provides the complete ESIT two-dimensional image (see Figure 8) similar to normal B-scan images. Since the ESCT image has more clarity in recognition of the point of nearest TOF, within the hyperbola of the diffracted signal from the tip, the line of interest for defect sizing is chosen as the maximum of the hyperbola over the region of signal and not exactly over the starting of the defect signal (as in case of manual sizing). The total processing time for converting the entire B-scan image to ESIT is less than a millisecond and almost a realtime process.

Table 3. Comparison of actual, manual and automatic defect sizing of vertical defects in 13 mm mild steel sample for 5 MHz and 10 MHz frequencies. The % error calculated based on the actual EDM notch dimensions Actual size (mm)

Manual sizing (mm)

ESIT based Automatic sizing (mm)

5 MHz

% Error

10 MHz

% Error

5 MHz

% Error

10 MHz

% Error

6.50

5.60

13.85

6.18

4.92

6.23

4.15

6.45

0.80

3.25

2.90

10.77

3.10

4.62

3.20

1.54

3.21

1.23

1.60

1.10

31.25

1.76

10.00

1.50

6.25

1.56

2.50

0.80

0.61

23.75

0.87

8.75

0.78

2.5

0.84

5.00

for both manual and automatic measurements gave results close to the actual defect size. It also appears that, for 2.25 MHz and 5 MHz frequencies the automatic defect sizing (ESIT) shows better accuracy than manual defect sizing. The validation of the ESIT technique was performed on a thicker sample (13 mm mild steel) with EDM notches using 5 MHz and 10 MHz, there the echoes were well separated (as shown in Figure 2) and hence manual sizing method can also be used effectively, particularly in the 10 MHz B-scans. Table 3 shows comparison of actual, manual and automated defect sizing on a 13 mm mild steel sample with vertical defects. The results show that the ESIT performs better than the manual sizing. At an average the ESIT shows an improvement of 80% using 5 MHz and 70% using 10 MHz over the manual defect sizing. The experimental results were compared with the commercial system qualitatively and quantitatively. Figure 10 shows qualitative comparison of B-scan images for 10 mm-thick aluminium sample with various vertical defects using the present set-up, manual AEA system and ESIT processed images. From the experimental results using the ESIT, an average accuracy of 93% can be achieved against an 84% using the manual sizing technique.

Figure 8. ESIT B-scan images of vertical defects in 10 mm-thick aluminium sample with defects of size (a) 6.5 mm and 3.25 mm, using 5 MHz, (b) 1.6 mm and 0.8 mm using 5 MHz, (c) 6.5 mm and 3.25 mm, using 10 MHz and (d) 1.6 mm and 0.8 mm using 10 MHz

The ESIT can be applied to (a) identify the superimposed signals that are always encountered in thin sections and when the critical flaw size is very small in thick sections and (b) automatically identify and size the defect to avoid manual errors and can be applied to both thin as well as thick sections. It is expected that this ESIT technique should work well for most cases. However, in the event that the phase, the amplitude and the decay rate (a) of the two superimposing signals are such that there is no apparent change in the shape of the superimposed signal, this technique may have its limitations. This situation is unlikely in realistic samples. However in order to avoid the possibility of missing defects, it is recommended that B-scan data be used in combination with the ESIT. One possible approach would be to use B-scan for defect detection and ESIT for defect sizing.

Experiments on thin fatigue samples Experiments were carried out using 5 and 10 MHz frequencies, 60∞ and 55° probes and 24-27 mm distances between probes. The B-scan Table 4. Experimentally measured crack depth using manual and automatic sizing in fatigue samples Manual Sizing (mm) Specimen Thickness (mm)

7. Results and discussion

5 MHz

Automatic Sizing (mm)

10 MHz

5 MHz

10 MHz

55 deg

65 deg

55 deg

65 deg

55 deg

65 deg

55 deg

65 deg

6.43

4.31

4.35

4.44

4.43

4.49

4.52

4.51

4.52

7.2

2.8

2.94

3.1

2.97

3.12

3.15

3.21

3.19

8.53

1.12

1.23

1.19

1.21

1.21

1.2

1.2

1.21

Experiments on EDM 10 mm-thick samples Experiments were carried out with the 10 mm thickness EDM notch aluminium and mild steel samples with simulated defects created using EDM (Figure 4). Experimental results (B-scan image) for vertical EDM notches in the aluminium samples are shown in Figure 8. Plot of defect number vs defect size that is measured using different frequencies are shown in Figure 9. The results shows that for the data obtained using the 10 MHz probes

(a)

(b)

(c)

Figure 9. Comparison of actual, manual and automatic defect sizing of vertical defects in 10 mm aluminium sample for various probe angle, probe separation and for (a) 2.25 MHz, (b) 5 MHz and (c) 10 MHz frequencies. Defect numbers 1,2,3 and 4 are corresponding to 6.5 mm, 3.25 mm, 1.6 mm and 0.8 mm vertical defects respectively

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Figure 10. Qualitative comparison of B-scan images for 10 mm thick aluminium sample with the vertical defects D1=6.5 mm, D2=3.25 mm, D3=1.6 mm and D4=0.8 mm (a) using the present experimental setup, (b) using manual AEA system and (c) ESIT processed

images of three samples with ESIT are shown in Figures 11-13. In these cases, it is very difficult to locate the crack tip echo directly from the traditional TOFD B-Scan images because the tip diffracted echoes are superimposed with the lateral wave and backwall signals. However, ESIT separates these echoes and makes it possible to size the defect accurately. Figure 13 shows the effect of weld bead and crack initiator V-notch on the image. Due to a very sharp V-notch, the diffracted signal is clearly seen and superimposed with the

Figure 12. Sizing of 3.2 mm fatigue crack in 7.2 mm thickness weld sample; (a) ESIT B-scan image; (b) echo separated signals at the point of cursor and (c) zoomed portion of the defect using 5 MHz, 60° probe angle and 26 mm distance between probes

crack signal. The details of measured crack depth using manual and automatic sizing algorithm for 5 and 10 MHz, for 55° and 65°, and 24-27 mm distance between probes are shown in Table 4. The measured defect depth of the fatigue cracks in the thin samples using the ESIT was found to be 4.52 mm, 3.20 mm and 1.20 mm. Since the fatigue crack contains only one tip inside (surface-breaking crack), the sizing problem is made easier. The accuracy of ESIT is more because it picks up the diffracted echoes automatically instead of manual location of diffracted echoes. By doing so, manual errors are eliminated or reduced.

8. Summary

Figure 11. Sizing of 4.52 mm fatigue crack in 6.43 mm thickness weld sample; (a) ESIT B-scan image; (b) echo separated signals at the point of cursor and (c) zoomed portion of the defect using 5 MHz, 60° probe angle and 26 mm distance between probes

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An Embedded Signal Identification Technique (ESIT) was developed for signal identification and automatic defect sizing for TOFD of thin plate-like structures. Experiments were conducted on 10 mm-thick aluminium and mild steel calibration samples with EDM notches (24 different types) and on thin maraging steel weld samples (around 7 mm thickness) with fatigue cracks of different sizes. Manual and automatic defect sizing algorithms were compared on these samples at different frequencies and probe angles. The results using automatic defect sizing show better accuracy relative to manual sizing. The experimental results were compared using a commercial TOFD system and found to be comparable for 10 mm-thick calibration samples and works better for thin sections having realistic fatigue cracks. Better data collection, computer controlled scanning, repeatability of experimental results, automated sizing and thus improved accuracy are added advantages to the TOFD system proposed. We have not evaluated ESIT for inclusions and pores in this work. Since ESIT is a signal processing technique, it is felt that it would likely work well for such defects.

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Acknowledgements This work was supported by Vikram Sarabhai Space Centre (VSSC), Trivandrum. Special thanks to Dr B C Baumick and Mr Raveendran of VSSC, Prof C R L Murthy and Dr M R Bhat of Indian Institute of Science for generating fatigue crack in maraging steel samples and to Mr R Subbaratnam and Mr M Palaniappan of Indira Gandhi Centre for Atomic Research, for allowing use of their commercial TOFD system. References

Figure 13. Sizing of 1.2 mm fatigue crack in 7.5 mm thickness weld sample with weld bead; (a) ESIT B-scan image; (b) echo separated signals at the point of cursor and (c) zoomed portion of the defect using 5 MHz, 60° probe angle and 26 mm distance between probes

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1. Charlesworth, J P and Temple, J A G, ‘Engineering Applications of Ultrasonic Time of Flight Diffraction’, Second Edition. 2001, Research Studies Press Ltd. 2. M G Silk, ‘Defect sizing using ultrasonic diffraction’, British Journal of NDT, Vol 21, No 1, pp 12-15, January 1979. 3. M G Silk, ‘The use of diffraction-based time-of-flight measurements to locate and size defects’, British Journal of NDT, Vol 26, pp208-213, May 1984. 4. M G Silk, ‘The interpretation of TOFD data in the light of ASME XI and similar rules’, British Journal of NDT, Vol 31, No 5, pp242-251, May 1989. 5. J A Ogilvy and J A G Temple, ‘Diffraction of elastic waves by cracks: application to time-of-flight inspection’, Ultrasonics, pp 259-269, November 1983. 6. M G Silk, ‘Benefits of signal processing in ultrasonic inspection’, INSIGHT, Vol 36, No 101, pp 776-781, October 1994. 7. Young-Fo Chang and Cheng-I Hsieh, ‘Time Of Flight Diffraction Imaging for Double-Probe Technique’, IEEE Transactions On Ultrasonics, Ferroelectrics, and Frequency Control, Vol 49, No 6, pp776-783, June 2002.

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