A Methodology to Automatically Segment 3D Ultrasonic Data Using X-ray Computed Tomography and a Convolutional Neural Network

A Methodology to Automatically Segment 3D Ultrasonic Data Using X-ray Computed Tomography and a Convolutional Neural Network

Ultrasonic non-destructive testing (UT) is a proficient method for detecting damage in composite materials; however, conventional manual testing procedures are time-consuming and labor-intensive. We propose a semi-automated defect segmentation methodology employing a convolutional neural network (CN...

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Bibliographic Details
Main Authors: Juan-Ignacio Caballero, Guillermo Cosarinsky, Jorge Camacho, Ernestina Menasalvas, Consuelo Gonzalo-Martin, Federico Sket
Format: Article
Language:English
Published: MDPI AG 2023-05-01
Series:Applied Sciences
Subjects:
machine learning
deep learning
ultrasounds
phased array
XCT
data fusion
Online Access:https://www.mdpi.com/2076-3417/13/10/5933

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