Cross Programme Talk: Explainable Augmented Intelligence (AI) for Crack Characterization

Add to your list(s) Download to your calendar using vCal

• Larissa Fradkin (Sound Mathematics Ltd)
• Friday 12 May 2023, 11:00-12:00
• Seminar Room 1, Newton Institute.

If you have a question about this talk, please contact nobody.

MWS - Mathematical theory and applications of multiple wave scattering

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 presentation describes 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 other similar datasets. The presentation discusses 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.

This talk is part of the Isaac Newton Institute Seminar Series series.

This talk is included in these lists:

• All CMS events
• Featured lists
• INI info aggregator
• Isaac Newton Institute Seminar Series
• School of Physical Sciences
• Seminar Room 1, Newton Institute
• bld31

Note that ex-directory lists are not shown.