Literature DB >> 26773788

Evaluation of near-surface stress distributions in dissimilar welded joint by scanning acoustic microscopy.

Dong Ryul Kwak1, Sanichiro Yoshida2, Tomohiro Sasaki3, Judith A Todd4, Ik Keun Park5.   

Abstract

This paper presents the results from a set of experiments designed to ultrasonically measure the near surface stresses distributed within a dissimilar metal welded plate. A scanning acoustic microscope (SAM), with a tone-burst ultrasonic wave frequency of 200 MHz, was used for the measurement of near surface stresses in the dissimilar welded plate between 304 stainless steel and low carbon steel. For quantitative data acquisition such as leaky surface acoustic wave (leaky SAW) velocity measurement, a point focus acoustic lens of frequency 200 MHz was used and the leaky SAW velocities within the specimen were precisely measured. The distributions of the surface acoustic wave velocities change according to the near-surface stresses within the joint. A three dimensional (3D) finite element simulation was carried out to predict numerically the stress distributions and compare with the experimental results. The experiment and FE simulation results for the dissimilar welded plate showed good agreement. This research demonstrates that a combination of FE simulation and ultrasonic stress measurements using SAW velocity distributions appear promising for determining welding residual stresses in dissimilar material joints.
Copyright © 2016 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Dissimilar welded plate; Leaky surface acoustic wave; Scanning acoustic microscopy; Surface stress

Year:  2015        PMID: 26773788     DOI: 10.1016/j.ultras.2015.12.006

Source DB:  PubMed          Journal:  Ultrasonics        ISSN: 0041-624X            Impact factor:   2.890


  1 in total

1.  Sparse Reconstruction for Micro Defect Detection in Acoustic Micro Imaging.

Authors:  Yichun Zhang; Tielin Shi; Lei Su; Xiao Wang; Yuan Hong; Kepeng Chen; Guanglan Liao
Journal:  Sensors (Basel)       Date:  2016-10-24       Impact factor: 3.576

  1 in total

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