ASTM E3327/E3327M-21
(Guide)Standard Guide for the Qualification and Control of the Assisted Defect Recognition of Digital Radiographic Test Data
Standard Guide for the Qualification and Control of the Assisted Defect Recognition of Digital Radiographic Test Data
SIGNIFICANCE AND USE
5.1 This guide describes the recommended procedure for using software to assist with the identification of indications in digital radiographic images. Some of the concepts presented may be appropriate for other nondestructive test methods.
5.2 When properly applied, the methods and techniques outlined in this guide offer radiographic testing practitioners the potential to improve inspection reliability, reduce inspection cycle time, and harness inspection statistics for improving manufacturing processes.
5.3 The typical goal of a nondestructive test is to identify flaws that exceed the acceptance criteria. Due to the variability and uncertainty present in any inspection process, acceptance thresholds are established so that some acceptable components are discarded in an effort to prevent parts with discontinuities that exceed the acceptance criteria from entering service. This type of error, called a false positive, is considered less critical than a false negative error which would allow a nonconforming part into service. A successful application of AssistDR minimizes the false positive rate while reducing the false negative rate to levels appropriate for the intended application. The methods and techniques described in this guide facilitate achieving this desired outcome.
5.4 With the advent of deep learning, convolutional neural networks, and other forms of artificial intelligence, scenarios become possible where an AssistDR system continues to evolve or learn after qualification for production use. This guide does not address learning-based AssistDR systems. This guide addresses only deterministic systems that have software code and parameters that are fixed after qualification. Note that this limitation does not prohibit the use of this guide for developing a qualification and usage strategy for software using deep learning technology. The training or learning process for the deep learning system would need to be completed before qualification and all ...
SCOPE
1.1 Assisted defect recognition (AssistDR) describes a class of computer algorithms that assist a human operator in making a determination about nondestructive test data. This guide uses the term AssistDR to describe those computer assisted evaluation algorithms and associated software. For the purposes of this guide, the usage of the words “defect,” “evaluate,” “evaluation,” etc., in no way implies that the algorithms are dispositioning or otherwise making an unaided final disposition. Depending on the application, AssistDR computer algorithms detect and optionally classify indications of defects, flaws, discontinuities, or other anomalous signals in the acquired images. Software that does make an unaided final disposition is classified as automated defect recognition (AutoDR). While the concepts discussed in this guide are pertinent to AutoDR applications, additional validation tests or controls may be necessary when implementing AutoDR.
1.2 This guide establishes the minimum considerations for the radiographical examination of components using AssistDR for non-film radiographic test data. Most of the examples and discussion in this guide are built around two-dimensional test data for simplicity. The principles can be applied to three (volumetric computed tomography, for example) or higher dimensional test data.
1.3 The methods and practices described in this guide are intended for the application of AssistDR where image analysis will aid a human operator in the detection and evaluation of indications. The degree to which AssistDR is integrated into the testing and evaluation process will help the user determine the appropriate levels of process qualification and control required. This guide is not intended for applications wishing to employ AutoDR in which there is no human review of the results.
1.4 This guide applies to radiographic examination using an X-ray source. Some of the concepts presented may be ap...
General Information
Standards Content (Sample)
This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the
Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.
Designation:E3327/E3327M −21
Standard Guide for
the Qualification and Control of the Assisted Defect
1
Recognition of Digital Radiographic Test Data
ThisstandardisissuedunderthefixeddesignationE3327/E3327M;thenumberimmediatelyfollowingthedesignationindicatestheyear
of original adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval.
A superscript epsilon (´) indicates an editorial change since the last revision or reapproval.
1. Scope priate for other nondestructive test methods when approved by
the AssistDR system purchaser.
1.1 Assisted defect recognition (AssistDR) describes a class
of computer algorithms that assist a human operator in making 1.5 Units—The values stated in either SI units or inch-
a determination about nondestructive test data. This guide uses pound units are to be regarded separately as standard. The
the term AssistDR to describe those computer assisted evalu- values stated in each AssistDR system may not be exact
ation algorithms and associated software. For the purposes of equivalents; therefore, each AssistDR system should be used
this guide, the usage of the words “defect,” “evaluate,” independently of the other.
“evaluation,” etc., in no way implies that the algorithms are
1.6 This standard does not purport to address all of the
dispositioning or otherwise making an unaided final disposi-
safety concerns, if any, associated with its use. It is the
tion. Depending on the application, AssistDR computer algo-
responsibility of the user of this standard to establish appro-
rithms detect and optionally classify indications of defects,
priate safety, health, and environmental practices and deter-
flaws, discontinuities, or other anomalous signals in the ac-
mine the applicability of regulatory limitations prior to use.
quired images. Software that does make an unaided final
1.7 This international standard was developed in accor-
disposition is classified as automated defect recognition (Au-
dance with internationally recognized principles on standard-
toDR).While the concepts discussed in this guide are pertinent
ization established in the Decision on Principles for the
to AutoDR applications, additional validation tests or controls
Development of International Standards, Guides and Recom-
may be necessary when implementing AutoDR.
mendations issued by the World Trade Organization Technical
Barriers to Trade (TBT) Committee.
1.2 This guide establishes the minimum considerations for
the radiographical examination of components usingAssistDR
2. Referenced Documents
for non-film radiographic test data. Most of the examples and
2
discussion in this guide are built around two-dimensional test
2.1 ASTM Standards:
data for simplicity. The principles can be applied to three
E1316 Terminology for Nondestructive Examinations
(volumetric computed tomography, for example) or higher
E1441 Guide for Computed Tomography (CT)
dimensional test data.
E1695 Test Method for Measurement of Computed Tomog-
raphy (CT) System Performance
1.3 The methods and practices described in this guide are
E2033 Practice for Radiographic Examination Using Com-
intended for the application ofAssistDR where image analysis
puted Radiography (Photostimulable Luminescence
will aid a human operator in the detection and evaluation of
Method)
indications. The degree to which AssistDR is integrated into
E2339 Practice for Digital Imaging and Communication in
the testing and evaluation process will help the user determine
Nondestructive Evaluation (DICONDE)
the appropriate levels of process qualification and control
E2422 Digital Reference Images for Inspection of Alumi-
required.This guide is not intended for applications wishing to
num Castings
employ AutoDR in which there is no human review of the
E2445/E2445M Practice for Performance Evaluation and
results.
Long-Term Stability of Computed Radiography Systems
1.4 This guide applies to radiographic examination using an
E2586 Practice for Calculating and Using Basic Statistics
X-ray source. Some of the concepts presented may be appro-
E2597/E2597M PracticeforManufacturingCharacterization
of Digital Detector Arrays
1
This guide is under the jurisdiction of ASTM Committee E07 on Nondestruc-
2
tive Testing and is the direct responsibility of Subcommittee E07.01 on Radiology For referenced ASTM standards, visit the ASTM website, www.astm.org, or
(X and Gamma) Method. contact ASTM Customer Service at service@astm.org. For Annual Book of ASTM
Current edition approved Dec. 1, 2021. Published February 2022. DOI: 10.1520/ Standards volume information, refer to the standard’s Document Summary page on
E3327_E3327M-2
...
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