Research in Nondestructive Evaluation, 26: 90–106, 2015Copyright © American Society for Nondestructive TestingISSN: 0934-9847 print/1432-2110 onlineDOI: 10.1080/09349847.2014.967900

ULTRASONIC DEFECT MAPPING USING SIGNAL CORRELATIONFOR NONDESTRUCTIVE EVALUATION (NDE)

Shanglei Li,1 Anish Poudel,2 and Tsuchin Philip Chu2

1Global Headquarters, Link Engineering Company, Plymouth, Michigan, USA2Department of Mechanical Engineering and Energy Process, Southern IllinoisUniversity, Carbondale, Illinois, USA

This article presents the application of a signal correlation technique to automatically classifyultrasonic A-scan signals for defect and defect-free regions in isotropic and anisotropic mate-rials. First, feature extraction was implemented by generating a reference A-scan signal ofa defect-free area using an autocorrelation function and statistics. Then, a cross-correlationfunction, utilized as a feature detector, was applied to the reference signal and a signal ofinterest (SOI) to detect defect-free features in an SOI. The correlation result was consideredas a pattern containing both defect and defect-free features. Next, the pattern was classi-fied by measuring the similarity between features of the reference signal and an SOI basedon their Euclidean distance. Each A-scan signal classification result was then plotted on a2D map based on its position on the specimen. The present work uses multiple correlationfunctions and statistics to classify defect signals rather than relying on an inspector’s priorknowledge to interpret C-scan data, and has particular value in automated ultrasonic signalclassification and characterization.

Keywords: A-scan signals, gating technique, defect mapping, NDE, signal correlation, UT

INTRODUCTION

Ultrasonic testing (UT) is considered to be a proven and a chosennondestructive evaluation (NDE) method, featuring large scale, speed, andnoncontact testing capabilities for the inspection of composites [1–8].Ultrasonic C-scan imaging technique is widely employed in many aerospacecomposite manufacturing and in-service operations for flaw detection andcharacterization [9]. A typical C-scan provides a map view of the specimenfeatures size and location. In C-scans, a data collection gate is utilized torecord A-scan signals (amplitude or time-of-flight) at regular intervals as thetransducer is scanned over the specimen. For each position where data isrecorded, the captured amplitude or time-of-flight (TOF) data is then nor-malized and displayed on a 2D image (grayscale or color) accordingly.Anomalous areas in the 2D images are caused by a significant drop in thepulse-echo/through-transmitted signal amplitude or the change in TOF ofa region, which are visible as a region that contrasts the surrounding area.

Address correspondence to Shanglei Li, Global Headquarters, Link Engineering Company,43855 Plymouth Oaks Blvd, Plymouth, MI 48170, USA. E-mail: s.li@linkeng.com

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ULTRASONIC DEFECT MAPPING USING SIGNAL CORRELATION 91

This classical C-scan requires prior-knowledge to set the gate at the correctlocation and the threshold with the proper value. A major drawback of thisgated-threshold approach is that the information contained in a full wave-form is not fully utilized; only the amplitude data (maximum absolute value ormaximum peak to peak value) within a pre-set gate is considered. In addition,another weakness of this amplitude-based approach is their direct and inher-ent sensitivity to nondefect related amplitude changes associated with themeasurement system or material sample, thus efficacy at low signal-to-noiseratio (SNR) is poor [10].

In this article, the authors focus on A-scan signal classification basedon signal detection theory. First, the A-scan signal was considered asan unknown random input signal. Then, signal pattern features such asfront echo, back echo, and defect echo were extracted by using a signalautocorrelation method. Based on a statistical method, a reference A-scansignal of the defect-free areas was generated. Next, features between thereference signal and the signal of interest (SOI) are analyzed based ontheir cross-correlation coefficients. In addition, the Euclidean distance wascomputed to measure the similarity between two signal patterns for signalclassification. After that, the classification result was plotted with the corre-sponding special positions to produce a 2D image of the specimen similar toa C-scan map.

A-SCAN CORRELATION

Autocorrelation of a random signal is described as a correlation betweenvalues of the same signal at different points in time [11]. In this study,an A-scan signal is considered as independent random variables in thetime domain; front echo and back echo are unrelated. Therefore, theautocorrelation process can be utilized to distinguish repeating patterns inultrasonic A-scan signals. The application of autocorrelation is to extractfeatures of defect-free A-scan signals by determining the locations of backechoes and their amplitude, respectively. Autocorrelation is the cross-correlation of a signal with itself, i.e., the correlation of a time series withits own past and future values. Instead of two different time series, theautocorrelation is computed between one time series and its replica shiftedby one or more time units. Consider an A-scan signal as a time series xi, N asthe number of points of the signal, and xi as the signal strength (amplitude)at i, correspondingly. The first-order autocorrelation coefficient is the simplecorrelation coefficient of the first N-1 observations xi, i = 1, 2, 3, . . . .., N − 1and the next N − 1 observations xi, i = 2, 3, 4, . . . .., N. The correlationbetween xi and xi+1 is given by

r1 =∑N−1

i=1 (xi − xα)(xi+1 − xβ

)√∑N−1

i=1 (xi − xα)2√∑N

i=2

(xi − xβ

)2 (1)

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where xα =∑N−1

i=1 (xi)

N−1 is the overall mean from x1 to xN−1, and xβ =∑N

i=2(xi)

N−1 isthe overall mean from x2 to xN.

For the first-order autocorrelation, the shift is one time unit. To obtain theautocorrelation function [6] for the overall signal length of an A-scan signal xi,Eq. (2) can be further generalized to give the correlation between observationsseparated by k time steps:

rk =∑N−k

i=1 (xi − xα)(xi+k − xβ

)√∑N−k

i=1 (xi − xα)2√∑N

i=k+1

(xi − xβ

)2 (2)

where xα =∑N−k

i=1 (xi)

N−k is the overall mean of the first N-k observations of xi, and

xβ =∑N

i=k+1(xi)

N−k is the overall mean of the last N-k observations of xi.

SIGNAL CLASSIFICATION

In order to classify the features of the defect and defect-free signals,the classification strategy is implemented by measuring the similarity of theSOI features and reference signal features with Euclidean distance. On ann-dimensional Euclidean space Rn, the intuitive notion of the length of thevector X = (X1, X2 · · · · · · Xn) is given by the formula [12]

||x|| =√

x21 + x2

2 + · · · · · · + x2n. (3)

MODELING AND SIMULATION

Consider that a back echo signal is an ideal impulse which can be sim-plified as a sinusoidal signal from 0 to 2π . Let x (t) = x0 sin (ωt + θ) be thegenerated defect-free reference signal and y (t) = y0 sin (ωt + θ + ϕ) be anSOI. x0 and y0 are amplitude coefficients of the reference signal and an SOI,respectively; ω and θ are the angle velocity and initial phase of a sinusoidalsignal; ϕ is the displacement (UT signal TOF variance) between the referencesignal and an SOI; and T0 = 2π

ωis the period of the sine function. The τ time

steps correlation function Rxy (τ ) of x (t) and y (t) can be expressed as

Rxy (τ ) = limτ→∞

1T

∫ T

0x (t) x (t + τ) dt

= 1T0

∫ T0

0x0y0 sin (ωt + θ) sin [ω (t + τ) + θ − ϕ] dt

= x0y0

2cos (ωt − ϕ) .

(4)

ULTRASONIC DEFECT MAPPING USING SIGNAL CORRELATION 93

Since x(t) is the back echo of the defect-free reference signal and y(t) isan SOI, then 0 ≤ y_0 ≤ x_0. This condition is only valid for a constant (uni-form) thickness materials, fixed focal point, and a fixed transducer’s field ofdepth. The main idea behind this mathematical model is to show and com-pare the defect detection “resolution” of traditional C-scan gate-thresholdmethod with the proposed correlation method under the similar circum-stances (same C-scan setup, same A-scan waveform) for the case presented.In order to better compare the gate-peak method and the proposed correla-tion method, considering x0 as a constant, the gate-peak method result canbe normalized as y0

x0, ranging from 0 to 1 with the maximum value x0 (when

y0 = x0). Similarly, normalizing Rxy (τ ) with the maximum value x202 , the new

correlation function can be expressed as

Rxy (τ ) = y0

x0cos (ωt − ϕ) (5)

The first part, y0x0

, is the amplitude ratio between the reference signal and anSOI; thus y0

x0∈ [0, 1]. The second part, the cosine function, indicates the cor-

relation changes in the variance of displacement ϕ, when the correlation stepsize ωt = 0 and the displacement ϕ = 0, Rxy (τ ) has the maximum value y0

x0,

which is the same as the normalized amplitude of the gate-peak method. Inthe gate-peak method, to compensate the thickness variance and the place-ment of the specimen during the UT test, the width of an applied gate isnormally wider than the back echo signal (usually a few microseconds). Forthe gate-peak method, when an SOI has the same amplitude coefficient asthe reference signal (x0 = y0), no difference will be detected, as long as thedisplacement ϕ stays within the gate range. In the correlation method, themaximum correlation coefficient can be achieved when step size ωt equals 0(no shift between two signals), where Rxy (τ ) becomes y0

x0cos (−ϕ). As shown

in Fig. 1(a), the correlation coefficient Rxy decreases from 1 to 0 in just ahalf period (0 ≤ ϕ ≤ π ) of a simulated SOI back echo signal, even where thereference signal and an SOI has the same amplitude.

Considering a back echo signal of the generated reference is sin (t).An SOI with an amplitude coefficient α and displacement δ with referencesignal can be expressed as αsin (t + δ), where 0 ≤ α ≤ 1 and 0 ≤ δ ≤ π .According to Eq. (3), the Euclidean distance DE is calculated as

DE =√

x21 + x2

2 + · · · · · · + x2n

=√∫ π

0(sin (t) − α sin (t + δ))2dt

=√

π

2

(α2 + 2α cos (δ) + 1

).

(6)

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FIGURE 1. Surface plots: (a) correlation function Rxy and (b) Euclidean distance DE.

As shown in Fig. 1(b), with both variances of amplitude α and displacementδ, the Euclidean distance DE has a range from 0 (α = 1 and δ = 0) to 2.5 (α =1 and δ = π ). With fixed displacement δ = 0 (no shift between two signals)and varying amplitude α, the DE has the range from 0 to 1.253. Both scenarioshave better resolution than the traditional gate-peak method (resolution: 0 toα, 0 ≤ α ≤ 1).

EXPERIMENTAL SETUP

The experiment was conducted in both isotropic and anisotropic materi-als to show the viability of the proposed method. The isotropic material wasconsidered to demonstrate the A-scan signal correlation procedure. The spec-imen considered was an aluminum 7075 block with twelve flat bottom holesof varied diameter drilled at different depths as shown in Fig. 2.

To verify the robustness of the signal correlation method on anisotropicmaterials, two different commercial aerospace graphite/epoxy compositepanels with predefined phantom defects, specifically, delamination defectsdue to impact damage and foreign object inclusions (FOIs) were considered.The defect maps of the composite panels used are shown in Fig. 3. The firstpanel as shown in Fig. 3(a) was a fiber-reinforced 22-ply composite laminateswith a symmetric orientation [0/+45/0/-45/90]S with nominal thickness of0.175 inches (4.45 mm). This panel had three impact damages that weresimulated by impacting it with hemispherical impactor at different knownenergies. The second panel as shown in Fig. 3(b) was a fiber-reinforced 16-ply composite laminates with a symmetric orientation [[0/45/0/45]2]S withnominal thickness of 0.132 inches (3.36 mm). This panel consisted of 12 rect-angular inserts of Teflon film (thickness = 0.004 inches) with changeabledistances inserted in different layers. The Teflon inserts in column I wereinserted between layer 1 and 2, column II were inserted in layer 8 and 9,and column III were inserted at layer 15 and 16.

ULTRASONIC DEFECT MAPPING USING SIGNAL CORRELATION 95

(a) (b)

All dimensions are in inches.

FIGURE 2. 7075 Aluminum (Al) specimen with flat bottom holes: (a) optical image and (b) CAD drawing.

All dimensions are in mm.

(a) (b)

FIGURE 3. Defect maps of commercial aerospace graphite/epoxy composite panels: (a) delaminationdefects due to impact damage nad (b) foreign object inclusions (FOIs).

A 5 MHz dual element Panametric transducer with a 2 inch focal lengthwas utilized in a pulse-echo mode for the immersion ultrasonic inspectionof these composite panels. The standoff distance between the transducer andthe panel was set to 2 inches and the scan was conducted at an increment of0.01 inches with index resolution of 0.01 inches.

EXPERIMENT PROCEDURE

Autocorrelation

As shown in Fig. 4(a), a defect-free A-scan signal ranging from -1 to 1 voltis represented by 128 data points indicating 5.07 μs elapsed time. Figure 4(b)

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(a) (b)

(c) (d)

Front echoBack echo First peak

Second peak

First peakBack echo peaks

Front echo

Back echoes

FIGURE 4. Defect and defect-free A-scan signal and autocorrelation coefficient: (a) defect-free A-scansignal, (b) autocorrelation of (a), (c) defect A-scan signal, and (d) autocorrelation of (c).

is the autocorrelation coefficient plot of the defect-free signal as shown inFig. 4(a). In Fig. 4(b), the first peak on the left side is due to the zero shiftbetween the original and replica signals, where the maximum value of the firstpeak always equals one. This makes signal alignment with autocorrelationcoefficient much easier than using a raw A-scan signal. The second peak inFig. 4(b) is the correlation result between the front and back echoes. The xaxis coordinate of the maximum peak value indicates the shift (delay steps) ofthe replica A-scan signal, which represents the distance (data digits) from thefront echo to back echo.

Statistics

Assuming that the majority of the specimen is defect-free, the defectregion can be defined as the localized area. Thus, in autocorrelation coef-ficients, the maximum value in the distribution of the second peak will bethe autocorrelation result of front and back echoes (as shown in Fig. 4(b)), orfront and defect echoes (as shown in Fig. 4(d)).

ULTRASONIC DEFECT MAPPING USING SIGNAL CORRELATION 97

FIGURE 5. Statistical distributions of correlation coefficient: (a) autocorrelation coefficient distributionand (b) back echo distribution of autocorrelation coefficient.

Figure 5(a) is the histogram of the second peak correlation coef-ficient distribution of the whole specimen. As depicted in the figure,most autocorrelation coefficients were within the range of 0.8–1. Theautocorrelation coefficient value associated with the highest count for theback echo signal in Fig. 5(a) was 0.8627. The distribution of the backecho distance associated with the maximum autocorrelation coefficient value(0.8627) was plotted in Fig. 5(b). Based on the assumption in the previousparagraph, the distance plot in Fig. 5(b) should include the back and defectechoes of the specimen. Figure 5(b) shows that the majority of the backechoes/peaks of A-scan signals associated with the maximum autocorrelationcoefficient value (0.8627) were 82 digits away from the front echoes/peaks.Therefore, for this specimen in which the majority is defect-free, the value82 represents the distance from the front echo to the back echo in a defect-free area. Any signal that has a distance from front to back echo differing fromthat of the majority of signals represents correlation results with defect echoesor noise. A defect-free A-scan signal can be identified based on the maximumautocorrelation value of the second peak and the distance at the maximumback echo distribution.

Signals satisfied both criteria (autocorrelation coefficient value and backecho distance) were considered as predetermined defect-free signals. To gen-erate a reference defect-free A-scan signal, a mean of these predeterminedA-scan signals needs to be calculated. During the UT test, a signal shift (usu-ally a few microseconds) can be generated due to the surface roughness ora misalignment of the specimen. This creates nonuniformity in the A-scansignals and greatly affects the overall performance of the generated referencesignal. Figure 6(a) is the mean of predetermined defect-free A-scan signals.Compared to the front echo amplitude (0.8 v) of the original defect-free A-scan signal in Fig. 4(a), the front echo of the generated reference signal hasdropped to 0.3 v in Fig. 6(a).

To obtain a better accuracy of the generated A-scan reference signal,A-scan signals were start-point aligned with the mean of pre-determined

98 S. LI ET AL.

(a) (b)

Front echoBack echo

Front echoBack echo

FIGURE 6. Generated A-scan reference signal of defect-free area: (a) without alignment and (b) withalignment.

defect-free A-scan signals (Fig. 6(a)) based on the maximum peak of the frontecho [13]. Fig. 6(b) is the mean of predetermined defect-free A-scan signalsafter alignment. As depicted in Fig. 6(b), the front echo of the aligned sig-nal has the same amplitude (0.8 v) as the original defect-free A-scan signalshown in Fig. 4(a). Thus, signal in Fig. 6(b) can be considered as the referenceof defect-free A-scan signal. Similar to the reference A-scan signal in Fig. 6(b),the signal in Fig. 7(c) is obtained from the mean of the predetermined defect-free autocorrelation coefficients. These generated references can be utilizedas the pattern of defect-free area. The autocorrelation of generated referencesignal in Fig. 7(c) is employed as the feature to classify defect-free and defectsignals.

Signal Classification

To detect defect-free features (shown in Fig. 7(c)), cross-correlationbetween generated autocorrelation reference and autocorrelation of the SOIwas computed [14]. The cross-correlation coefficients between generated ref-erence and A-scan SOI autocorrelation are shown in Fig. 7(b) and Fig. 7(e),respectively. The cross-correlation is computed by a sample-shift along oneof the input signals. Therefore, there are 256 data points in cross-correlation,which double the input signal points (128). The left peak in Fig. 7(b) is thecross-correlation result between the first peak of the defect-free reference (inFig. 7(c)) and the second peak of the SOI as shown in Fig. 7(a). The mainpeak in the middle in Fig. 7(b) shows the cross-correlation by using the wholelength of the reference and the SOI. The right peak in Fig. 7(b) represents thecross-correlation between the first peak of the SOI and the second peak ofthe reference signal. Thus, the coefficients in Fig. 7(b) not only contain partialcorrelation results of the first and second peaks (front and back echoes), butalso carry the correlation information of the whole signal.

ULTRASONIC DEFECT MAPPING USING SIGNAL CORRELATION 99

(a) (b)

(c)

(d) (e)

First peakSecond peak

First peakSecond peak

First peakBack echo peaks

Left peak Right peak

Left peaks

Right peaks

FIGURE 7. Cross-correlation of the reference signal and A-scan signal of interest: (a) autocorrelation ofdefect-free A-scan signal, (b) cross-correlation of generated reference and (a), (c) reference autocorrelationof defect-free area, (d) autocorrelation of defect A-scan signal, and (e) cross-correlation of generatedreference and (c).

Figure 7(e) is the cross-correlation coefficient between the generateddefect-free reference and a defect signal (autocorrelation). As depicted inFig. 7(e), defect signal cross-correlation results in low correlation coef-ficients and multiple left and right peaks due to the properties of thewaveforms and the location of the first peak (front echo) and back echopeaks as shown in Fig. 7(d). The cross-correlation coefficients betweenreference and interest signal is utilized as features to classify defect-free and defect signals. The classification is implemented by measuringthe similarity of signals of interest features and reference signal fea-tures with Euclidean norm, shown in Eq. (3). Thus, the similarity wasmeasured at each point with the whole length of the cross-correlationcoefficient.

As shown in Table 1, the average range of the Euclidean norm of defect-free signal was from 0.037 to 0.073, the defect signal had relatively greaterEuclidean norm value ranging from 1.775 to 1.916. The average defectEuclidian value 1.8 is 30 times the average (0.05) of defect-free Euclidianvalue.

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TABLE 1 Euclidean norm of defect and defect-free signal

Defect-free signal Defect signal

1 0.037 1.9032 0.056 1.9163 0.044 1.8754 0.061 1.8525 0.073 1.775

RESULT AND DISCUSSION

Figure 8 shows the results obtained for the 7075 aluminum test specimenwith 12 flat bottom holes. As depicted in Fig. 8, the classified results ofA-scan signals were mapped on a 2D image according to the measured A-scan locations of the specimen. The image resolution of mapped aluminumresult is 180 × 280 pixels. Figure 8(a) is the C-scan image generate basedon gated-peak detection (the maximum absolute peak values of the backechoes). The cross-correlation coefficient result and its inverted image aredepicted in Fig. 8(b) and Fig. 8(c), respectively. The A-scan signals associatedwith dark regions in Fig. 8(b) indicate they have less Euclidean norm (highlycorrelated) with the generated defect-free reference signal. On the contrary,the signals in the bright region have large Euclidean norm, which indicatethey are less correlated with the defect-free reference signal. Therefore, whenthe raw cross-correlation result was plotted into an 8-bit grayscale image(256 gray level), defect regions were plotted in bright and defect-free regionswere plotted in dark. To provide an image similar to a C-scan result, the rawcross-correlation image in Fig. 8(b) was inverted as shown in Fig. 8(c).

FIGURE 8. Comparison of result images of aluminum sample: (a) C-scan image generated by using 5 MHzimmersion UT system, (b) raw cross-correlation image, and (c) inverted image of (b).

ULTRASONIC DEFECT MAPPING USING SIGNAL CORRELATION 101

FIGURE 9. Comparison of result images in a commercial graphite/epoxy laminate specimen with delam-ination defect due to impact damage: (a) C-scan image generated by using 5 MHz immersion UT system,(b) raw cross-correlation plot image, and (c) inverted image of (b).

The results of delamination due to impact damage and FOIs in commer-cial graphite/epoxy laminate specimens are shown in Fig. 9 and Fig. 10,respectively. The image resolution of mapped impact damage specimen was340 × 740 pixels. The C-scan area in Fig. 10 covers the lower 3 FOIs in theFOI specimen column III (Fig. 3). The image resolution of mapped specimenis 264 × 689 pixels. As shown in the above figures, the signal correlationmethod is capable to detect delamination and foreign object in anisotropicmaterials without using prior knowledge. For subjective evaluation, the cor-relation results obtained demonstrated that the applied method can greatlyimprove the image quality and offer high contrast image by restraining thenoises effectively.

In addition to the perceived image quality with human visual system(HVS), for objective evaluation, peak signal-to-noise ratio (PSNR) and con-trast signal-to-noise ratio (CNR) are employed for quantitative assessment.The original C-scan results (Fig. 9(a) and Fig. 10(a)) are compared with themapped correlation results (Fig. 9(c) and Fig. 10(c)) accordingly to evaluatethe image quality and robustness of the correlation method.

The PSNR is given as

PSNR = 10 · log10

(MAXI

2

MSE

)(7)

where MAXI is the maximum possible pixel value of the image. In this study,all pixels are represented using 8 bits gray level, here MAXI is 255, and MSEis the mean squared error between two compared images.

102 S. LI ET AL.

FIGURE 10. Comparison of result images in a commercial graphite/epoxy laminate specimen with for-eign object inclusions: (a) C-scan image generated by using 5 MHz immersion UT system, (b) rawcross-correlation plot image, and (c) inverted image of (b).

The CNR is given as

CNR = Si − So√σ 2

i + σ 2o

(8)

where Si and So are the mean values inside and outside the ROI, respectively,and σi and σo are the standard deviations, respectively.

From Table 2, it can be verified that the CNR of the correlation resultis higher than the original C-scan result. The CNR value of correlationresults has an average increase of five times compared to the original C-scanresults. The correlation method provides better performance in CNR, indicat-ing boundaries/outlines between defect and defect-free area are clear to see.Further, note that the PSNR value of 3 specimen test is close to each other,which indicates the quality of the proposed correlation method is robust and

TABLE 2 Experimental comparison of CNR and PSNR

Specimen ResultsCNR(dB)

PSNR(dB)

12 Hole Sample(Aluminum Block)

Original C-scan resultsCorrelation result

3.52 56.5723.91

Impact Damage Sample(composite panel)

Original C-scan resultsCorrelation result

5.58 50.8228.30

F. O. Sample(composite panel)

Original C-scan resultsCorrelation result

18.27 50.5287.81

ULTRASONIC DEFECT MAPPING USING SIGNAL CORRELATION 103

FIGURE 11. Canny edge detection results for blind holes in Al sample for (a) C-scan result and (b)correlation result.

reliable. Since the applied method provides good performance in both CNRand PSNR, it can be seen that the proposed correlation classification methodis effective in defect detection.

For quantitative evaluation, the Canny edge detector [15] with the sameparameters (threshold: default; smooth filter size: 3) were applied to bothC-scan and correlation results. From the results shown in Fig. 11(a), it isdemonstrated that the edge detection is limited in the traditional C-scan gat-ing technique. In addition to the blind hole edges, outer edges inside/outsideof the hole areas were also observed, whereas, in Fig. 11(b), only the blindhole’s edges were identified.

Figures 12(a) and (b) show C-scan and correlation results for the top threeblind holes of Al sample respectively for quantitative evaluation. As shownin Figs. 12(a) and (e), ringing artifacts (aurora) were observed in the first andthe third blind holes. These were caused due to the diffraction effects alongthe sharp edges of the blind holes. Figures 12(c) and (d) are data plots inrow 88 of C-scan and correlation results, respectively. The Euclidean distancein Fig. 12(d) is reversed (Max-individual value) to match the comparison ofC-scan amplitude data. In Fig. 12(c), the large data increase in the middle ofdefect 1 and 3 indicates the strong edge diffraction effects. The dynamic rangeof defect and defect free signals for correlation results were much higher com-pared to the traditional C-scan gating signals in Fig. 12(b). For correlationtechnique, the data range were from 0.067 to 2.237 with a standard devi-ation of 0.795. The non-defect signals were about 33.4 times higher thandefect signals. Similarly, for C-scan gating technique, the data range were

104 S. LI ET AL.

(a) (b)

(c) (d)

(e) (f)

1 2 3 1 2 3

FIGURE 12. Results for top 3 blind holes in Al sample: (a) C-scan gating technique; (b) correlation tech-nique; (c) line profile plot for amplitude data of C-scan shown in (a) (row 88); (d) line profile plot forEuclidean distance of correlation shown in (b) (row 88); (e) blind hole edges detected with C-scan; and (f)blind hole edges detected with correlation.

from 0.023 to 0.18 with a standard deviation of 0.049. The non-defect signalswere only 7.8 times higher than defect signals.

In addition, the blind hole diameter measurements were carried out bymeasuring the distance between the Canny edges result in Figs. 12(e) and (f).Comparisons of blind hole measurements of C-scan and correlation resultsare shown in Table 3.

As shown in Table 3, the correlation method has a less percentage error(8%) and better consistency (0.27 inch) in measured diameters, indicatingthe quality of the correlation result is more robust and reliable. Since theapplied method provides good performance in both measurement accuracyand percentage error, it can be seen that the proposed correlation method iseffective for defect detection and characterization.

The total computational time elapsed while implementing the classifica-tion of the A-scan signal correlation technique for 12 flat bottom hole Al

ULTRASONIC DEFECT MAPPING USING SIGNAL CORRELATION 105

TABLE 3 Measured defect diameters

PixelsEquivalent diameter

(inches)Actual diameter

(inches)Percentage

error

Defect1 diameter

C-scan result 34 0.34 0.25 36%Correlation

result28 0.27 0.25 8%

Defect2 diameter

C-scan result 31 0.31 0.25 24%Correlation

result28 0.27 0.25 8%

Defect3 diameter

C-scan result 34 0.34 0.25 36%Correlation

result28 0.27 0.25 8%

specimen was 41.25 seconds (180 × 280 A-scan signals), graphite/epoxycomposite panels with impact defects was 110.28 seconds (340 × 740 A-scan signals), and for the graphite/epoxy composite panels with FOIs was69.01 seconds (264 × 689 A-scan signals). Each A-scan signal that wasconsidered had 128 digit data points. All computations were performed inMATLAB R2013b (64-bit) environment installed on Win7 64-bit OS with IntelCore i7 quad core CPU @ 2.3 GHz, 6MB cache, 8 GB RAM.

CONCLUSIONS

The experimental results presented in this article show that the A-scan sig-nal correlation method is capable of mapping defects on 2D images by usingthe whole information of A-scan signals rather than only using the maximumabsolute value of the back echo within the preset gate. Without using theprior knowledge of the back echoes, the proposed method generated imagessimilar to a C-scan based on statistics and signal classification. The fidelityof this method has been verified by isotropic material (aluminum 7075) andanisotropic material (graphite/epoxy composite laminates). The experimentalresults obtained for different materials with different defects have demon-strated the effectiveness of the applied method. This A-scan signal correlationmethod has particular value in automated ultrasonic signal classification andintelligent ultrasonic NDE systems.

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