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Point cloud-based elastic reverse time migration for ultrasonic imaging of components with vertical surfaces

journal contribution
posted on 22.06.2021, 09:23 by Jing Rao, Jilai Wang, Stefan Kollmannsberger, Jianfeng Shi, Hailing FuHailing Fu, Ernst Rank
This work presents a new ultrasonic imaging framework for non-destructive evaluation of components with vertical or steeply dipping surfaces and demonstrates its ability of accurately characterizing multiple defects hidden in the interior of the component based on a limited coverage of ultrasonic linear phased array. Central to the framework is a point cloud-based elastic reverse time migration (PC-based ERTM) method. First, a surface reconstruction is derived from the point cloud provided through photos of an object from multiple views by bundle adjustment. Second, by taking the surface reconstruction as a geometric background estimate for elastic reverse time migration, the algorithm considers information of multiple scattering and mode conversions as well as multiple wave reflections from the component’s bottom and aims at detecting internal defects. The effectiveness and accuracy of the PC-based ERTM approach is examined by experiments with multiple defects in extruded aluminum specimens with vertical surfaces. Experimental results show that notches and side-drilled holes in components can be reconstructed accurately.

Funding

Alexander von Humboldt Foundation under grant number 1022809

German Research Foundation (DFG) under the Grant No. Ra624/29-1 and KO4570/1-1

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Mechanical Systems and Signal Processing

Volume

163

Publisher

Elsevier BV

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal Mechanical Systems and Signal Processing and the definitive published version is available at https://doi.org/10.1016/j.ymssp.2021.108144.

Acceptance date

10/06/2021

Publication date

2021-06-20

Copyright date

2021

ISSN

0888-3270

Language

en

Depositor

Dr Hailing Fu. Deposit date: 21 June 2021

Article number

108144