Abstract
Crack characterization is one of the central tasks of NDT&E (the Non-destructive Testing and Evaluation) of industrial components and structures. These days data necessary for carrying out this task are often collected using ultrasonic phased arrays. Many ultrasonic phased array inspections are automated but interpretation of the data they produce is not. This paper offers an approach to designing an explainable AI (Augmented Intelligence) to meet this challenge. It describes a C code called AutoNDE, which comprises a signal-processing module based on a modified total focusing method that creates a sequence of two-dimensional images of an evaluated specimen; an image-processing module, which filters and enhances these images; and an explainable AI module—a decision tree, which selects images of possible cracks, groups those of them that appear to represent the same crack and produces for each group a possible inspection report for perusal by a human inspector. AutoNDE has been trained on 16 datasets collected in a laboratory by imaging steel specimens with large smooth planar notches, both embedded and surface-breaking. It has been tested on two other similar datasets. The paper presents results of this training and testing and describes in detail an approach to dealing with the main source of error in ultrasonic data—undulations in the specimens’ surfaces.
Highlights
C code called AutoNDE, which comprises a signal-processing module based on a modified total focusing method that creates a sequence of two-dimensional images of an evaluated specimen; an image-processing module, which filters and enhances these images; and an explainable Augmented Intelligence (AI)
The aim of this paper is to address a challenge of developing an explainable AI for semi-automatic crack characterization, with a view to its ultimate deployment in ultrasonic units for NDT&E of industrial components and structures
Experiment are realistic, but our analysis confirmed that they have to be modeled differently, The CIVA code was provided with precise descriptions of both the inspection surface and backwall obtained with a flexible probe
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. In this paper we present an alternative: a code that combines a signal processing algorithm based on a simple modification of the TFM with the well-known image processing algorithms as well as a decision tree. The latter is an AI module, which mimics thought processes followed by human inspectors in writing standard inspection reports. We demonstrate the efficacy of the approach using laboratory data To collect such data engineers manufacture test blocks to contain flaws with known characteristics and use the the NDT procedure they want to investigate to establish whether it can generate reasonable estimates of these characteristics [1]. In the last section we discuss our findings and present recommendations
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