Crack characterisation is one of the central tasks of
NDT&E (Non-Destructive Testing and Evaluation) of industrial components and
structures. A novel code containing a decision tree, that is, an explainable AI
has been designed and developed for characterizing single large planar cracks.
For the component surfaces whose undulation errors can be described using a
normal distribution, a method for automatic estimation of the degree of the
interpolating polynomial was developed. 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 chapter offers an approach to designing an
explainable AI (Augmented Intelligence) to meet this challenge. It describes a
novel C++ code called AutoNDE, which contains 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 customized decision
tree, which selects images of possible cracks, groups those of them that appear
to represent the same crack and for each group, produces 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, establishing values of
various model parameters by trial and error It has been tested on two other
similar datasets. The chapter 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. AutoNDE locates a
number of points on the inspected surfaces and effects a polynomial surface
interpolation. Under the assumption that the error in the location of these
points obeys a normal distribution, a novel method is presented for automatic
estimation of the polynomial degree. Notwithstanding various challenges,
AutoNDE shows great promise, demonstrating the feasibility of an explainable
AI, suitable for applications in industrial NDE, increasing its accuracy and
efficiency.
Author(s) Details
Larissa Fradkin
Sound Mathematics Ltd., 11 Mulberry Close, Cambridge, CB4
2AS, UK.
Sevda Uskuplu
Altinbasak
Sound Mathematics Ltd., 11 Mulberry Close, Cambridge, CB4
2AS, UK.
Michel Darmon
Université Paris-Saclay, CEA, List, F-91120 Palaiseau,
France.
Please see the book here:- https://doi.org/10.9734/bpi/srnta/v6/2680
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