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Svahn, Per-Håkan; Virkkunen, Mikko; Zettervall, Tommy; Snögren, DanielThe use of virtual flaws to increase flexibility of qualification

Published in:12th European Conference on Non-Destructive Testing (ECNDT 2018)

Published: 01/08/2018

Document VersionPublisher's PDF, also known as Version of record

Please cite the original version:Svahn, P-H., Virkkunen, M., Zettervall, T., & Snögren, D. (2018). The use of virtual flaws to increase flexibility ofqualification. In 12th European Conference on Non-Destructive Testing (ECNDT 2018) (The e-Journal ofNondestructive Testing; Vol. 23, No. 8).

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The use of virtual flaws to increase flexibility of qualification

Per-Håkan Svahn1, Iikka Virkkunen2 Daniel Snögren3 and Tommy Zettervall4

1 SQC Swedish Qualification Centre, Sweden, per-hakan.svahn@sqc.se

2 Trueflaw, Finland, iikka.virkkunen@trueflaw.com 3 SQC Swedish Qualification Centre, Sweden, daniel.snogren@sqc.se

4 SQC Swedish Qualification Centre, Sweden, tommy.zettervall@sqc.se

Abstract

Representative test mock-ups and flaws are important part of reliable qualification.

Ideally, the qualification mock-ups should contain a sufficient number of various flaws

to cover variation in natural flaws and to give a reliable picture of the capabilities of the

inspection and its variability. However, providing sufficient mock-ups and flaws is

technically challenging and costly. The challenge of providing representative mock-ups

is common for all qualifications.

This project verified a novel method and software to modify inspection data and to

produce new virtual test specimens for data analysis. Further, objective was to get more

data to increase the basis for a reliable decision in the performance of inspection

qualification activities. The software is able to modify data and signals both from

Ultrasonic and Eddy Current techniques, and to be used on different mechanized

inspection and tube inspection systems.

The project investigated, if it is possible to produce new test specimens, flaws or grading

units, both with and without defects, in test block data files to be used in training and

qualification of personnel. The aim was also to improve the statistics and confidence level

based on an increased number of flaws. For this purpose it is necessary to have the

possibility to modify flaw shape and size within a certain tolerance to get a reliable

probability of detection (POD) curve.

The project also show a technique that could be used to design blind test blocks or defects

in a test block where defect signals are taken from an open one. This will have an positive

impact on the costs of producing new test blocks.

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1. Introduction

The qualification of NDT procedures traditionally requires representative mock-ups with

a significant number of representative flaws. For this purpose, extensive investment has

been made in Sweden and other countries over the years to form a library of test blocks

and flaws that nowadays cover well the common qualification cases. These test blocks

have been used successfully for decades of high quality qualifications now [1].

Despite the significant existing investment, there’s constant need for additional mock-ups

or flaws. Changes and development in inspection technology may change requirements

for the mock-ups. For example, current phased array ultrasonic methods are more

sensitive to noise caused by implantation welds than earlier methods, that were used when

the first mock-ups were manufactured. New degradation mechanisms or postulated flaw

types are sometimes found in power plants or added to the inspection requirements and

this necessitates additional qualifications, test blocks and flaws (see e.g. [2]). The

changing authority requirements or increasing reliability demand may increase demands

for additional flaws or test blocks. Finally, with increasing amount of qualifications

performed on limited set of test blocks, there’s increased risk of inspectors “learning the

mock-ups”. Consequently, despite the significant investment to existing mock-ups and

flaws, there’s constant need for additional investment.

The increased prevalence of risk informed in service inspection (RI-ISI) also poses

additional requirements to the traditional nuclear qualifications. To take full advantage of

the conducted inspections, the effect and value of the inspection should be described in

quantitative terms [3]. In particular, the probability of detection (POD) for the inspections

should be estimated. In the ENIQ methodology, the technical justification has been used

to great success to reduce the number of needed test blocks. Unfortunately, this has also

made the quantitative estimation of qualified performance difficult. The standard ways

[4,5] to estimate POD necessitate much more flaws than traditionally used in

qualification. Also, the required flaw distribution differs from that used in typical mock-

ups. Various approaches have been suggested for more quantitative evaluation of ENIQ-

style qualifications [6,7], but the subject remains open. Ideally, the qualification mock-

ups should contain a sufficient number of various flaws to give a reliable (in statistical

terms) picture of the capabilities of the inspection. This would allow direct statistical

estimation of achieved performance. The mock-ups should also provide a sufficient

number of defect-free areas to provide ample opportunity for detecting excessive false-

call rates.

Modern automated inspections, provide new opportunities to work around these

traditional limitations. With automated inspection, the data gathering and analysis are

separated into distinct steps. This separation allows new possibilities for training and

qualification. Because the analysis now operates, essentially, on pre-recorded data, the

need for different physical training samples and training data sets is also separated. The

data gathering can be developed and qualified on existing physical samples, and the

more demanding data analysis can be completed on a separate data set. The needs for

these two steps are quite different. For data gathering, a representative sample is needed,

but the problematic need for a high number of representative flaws primarily concerns

data analysis. Consequently, being able to modify the gathered data sets to include non-

3

existing virtual flaws offers several significant advantages: the number of physical test

blocks and flaws can be reduced, the number of flaws in the data can be increased to

give statistically significant results and the number of different data sets available can

be increased such that every trainee or qualification candidate receives a fresh data set.

This is the central idea of the eFlaw technology developed by Trueflaw [8,9]: a pure

flaw signal is extracted from flawed data set and then re-introduced into various

locations in the data set to provide a virtually unlimited number of different data files

for training and qualification purposes. The technology also lends itself to training [10]

and POD determination combining real acquired data and simulation [11,12].

There’s long history of using various “virtual flaws” for different training set-ups [13-

15] and the topic is under active development [16]. The Trueflaw implementation

focuses on modifying data-files from automated (or at least position-encoded) data files.

In this study, the previously developed eFlaw technology was further adapted for ENIQ-

style qualification as implemented by SQC in Sweden. The aim of the study was to:

• try out and confirm the applicability of eFlaw technology for nuclear

qualifications,

• explore the range of possibilities with modifications and

• to evaluate possible future potential of this technology.

To this end, the study included several features not previously demonstrated:

• consistent modifications in separate files (various UT and EC),

• modification of flaw length,

• modification of flaw depth,

• moving flaws from one mock-up to another,

• modification of ultrasonic parameters to make data from different files with

different acquisition parameters compatible.

The modifications were evaluated with standard SQC fingerprinting procedures to

confirm the readiness-level of various modifications.

2. Materials and methods

Three test blocks, “Inc-1”, “PCVC-3” and “PCVC-4” from SQC test block library were

selected as “flaw donors”. The test blocks contained various types of flaws, as shown in

Table 1. One of these test blocks, “Inc-1”, was selected as the “canvas” block to form the

basis of modifications. As it turned out, the acquisition parameters were different in Inc-

1 and in PCVC-3 and PCVC-4. Among other things, the scan and index resolutions were

different. Since the PCVC-3 and PCVC-4 acted as flaw donors to Inc-1 based canvases,

the data needed to be adjusted to account for these differences.

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Table 1. Summary of test blocks and flaws that acted as flaw donors.

Test block Description Use

INC-1 Dissimilar metal weld, with 12 flaws

(IDSCC simulation, implanted

fatigue cracks, EDM-notches.

Canvas + Donor

PCVC-3 Dissimilar metal weld, 4 implanted

real IDSCC flaws

Donor

PCVC-4 Dissimilar metal weld, 4 implanted

real IDSCC flaws

Donor

All of the test blocks were scanned using a number of different NDT techniques. These

are summarized in Table 2. Ultrasonic scans were completed with Zetec Dynaray

equipment and eddy current scans with Eddyfi Ectane 2 equipment. The corresponding

ultrasonic data files used the .UVData -file format from Zetec and eddy current data files

used .magdata -file format from Eddyfi. All analysis at SQC were done using native

analysis software (i.e. Ultravision or Magnifi). All data modifications were done with

Trueflaw in-house software with no association to respective equipment vendors.

Table 2. NDT methods.

Method File format Notes

Phased array ultrasonic .UVData CS side and SS side, OD

TRL-45° ultrasonic .UVData CS side and SS side, OD

TRL-55° ultrasonic .UVData CS side and SS side, OD

TOFD ultrasonic .UVData ID

Eddy current .magdata ID

SQC defined a set of target modifications. All the modifications were done so as to retain

coherency between different inspection data from different methods. Not all

modifications were applicable for all the methods. First a clean set of canvases was made

by removing all the flaw indications from Inc-1 data acquired with all the methods. These

clean canvases were then used as basis for the modifications. The modifications were

divided in two final canvas-sets, both containing multiple modifications and multiple

NDT-techniques. The specified modifications are summarized in Table 3.

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Table 3. Summary of flaw modifications.

Canvas 1

1 Move 6 x 20 flaw to another scan location

2 Move 26 x 50 flaw to another scan location

3 Move 17 x 50 flaw to new location and change size to 17 x 40 mm.

4 Move 3 x 6 flaw to new location

Canvas 2

5 Move 26 x 50 flaw to new location and change size to 20 x 36 mm.

6 Insert 11 x 36 from different donor to new canvas, different location and

change size to 9 x 36

7 Insert 18 x 35 flaw from different donor to new canvas and location

8 Insert 6 x 23 flaw from different donor to new canvas and location, change

size to 6 x 17

9 Insert 15 x 35 flaw from different donor to new canvas and location

10 Insert 10 x 32 flaw from different donor to new location, change size to

13 x 32

11 Insert 12 x 35 flaw from different donor to new location, change size to

14 x 35

12 Insert 5 x 35 flaw from different donor to new location

13 Insert 5 x 35 flaw from different donor to new location, change size to

3 x 25

In total, the modifications span 2 canvas sets, 13 modification in 8 separate NDT scans,

104 modifications in total. This data-set is sufficient to evaluate the various aspects

necessary for using this technique for NDT qualification.

3. Results and discussion

The created canvases were fingerprinted at SQC. Images 1 – 10 show typical inspection

data from the original file modified data. In the clean canvas creation, one challenge was

that the original mock-up contained rather limited defect-free area. During clean mock-

up creation, this area was used as a reference and due to the small defect free area, the

resulting files show repeating patterns.

6

Figure 1. Original Inc-1 canvas, as seen in Phased array 55° data.

Figure 2. Modified canvas 1, as seen in Phased array 55° data. Some regular shifts can be seen, especially clearly at root area due to limited unflawed area in the original data. Otherwise nice

appearance.

7

Figure 3. Original Inc-1 canvas, as seen in TRL 45° data.

Figure 4. Modified canvas 1, as seen in TRL 45° data. Some regular shifts can be seen, especially clearly

at root area due to limited unflawed area in the original data. Otherwise nice appearance.

8

Figure 5. Original Inc-1 canvas, as seen in eddy current data.

Figure 6. Modified canvas 1, as seen in eddy current data. Defect areas have been moved with little or

no signs of it. In some areas there is a small shift in balance point. One benefit of the modification is

that areas with material change in the cladding area that appear at the implant location can be filled in

to get a more realistic image that does not give away the defect positions.

9

Figure 7. Defect 4 in the original data.

Figure 8. Defect 4 in the modified data. Very similar appearance with correct amplitude and defect

profile.

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Figure 9. Defect 7 in the original data.

Figure 10. Defect 7 in the modified data. Height has been alterd correctly. Appearance of the defect is

quite different from the original because of a difference in scan resolution. This causes a difference in

applied soft gain of 10dB. However, it can be seen that altering the defect height works fine. The

difference in applied soft gain makes the defect area stand out somewhat in the modified data as can be

seen from the noise level. Very similar appearance with correct amplitude and defect profile.

During fingerprinting, the data was analysed in the express intention to evaluate

applicability for qualification. In particular, the following qualities were evaluated:

• Conformance to specification – does the modified files show the flaws specified

• Consistency – do the modifications in different data-files show similar

modifications

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• Representativeness of modifications – do the modified flaws show natural echo

dynamics in the A-scan level, do they look as expected with various plots and

techniques

• Absence of artefacts – do the modified files show disturbing signs or artefacts

resulting from the modification that might adversely affect the inspection.

At the same time, it was recognized, that all current flaw implantations also have

limitations, and thus the performance of the modifications is evaluated first and foremost

to confirm fitness for purpose.

The preliminary fingerprinting showed, that the technology is quite promising. Flaws

could be relocated consistently in different data-files and their length and height could be

altered successively. However, at this point, not all the alterations were successful and

some fine-tuning will be necessary. This indicates that the data modification requires

manual skill and needs to be done interactively with fingerprinting for best results.

Moving flaws from one mock-up to another was also successful, even when the data files

were scanned using different parameters. However, some features of the original scan

remained identifiable and thus it’s clearly advisable to use as similar flaw donors and

canvases as possible.

4. Conclusions

A set of flawed data files were modified for the purpose of evaluation the current status

of virtual flaws and, in particular, the eFlaw technology for using in NDE qualifications.

The evaluation used real-world data files from SQC and the modifications were in line

with qualification needs.

Overall, the method of creating virtual test blocks by moving defects and altering their

length and height is very promising. Some fine tuning needs were identified for UT to

smooth the areas around the defect so that it blends in better with defect free areas.

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