ASTM E3327/E3327M-21 PDF

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...