Standard Guide for the Qualification and Control of the Assisted Defect Recognition of Digital Radiographic Test Data

Importancia y uso:

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 parameters of the deep learning system held fixed (as with deterministic software approaches based on traditional image processing) after qualification and during use.

Subcomité:

E07.10

Volúmen:

03.04

Número ICS:

03.100.30 (Management of human resources), 19.100 (Non-destructive testing), 35.240.99 (IT applications in other fields), 37.040.25 (Radiographic films)

Palabras clave:

assisted defect recognition; automated defect recognition; computer assisted evaluation; digital radiographic images; indication;