University of Limerick
Browse
Kourousis_2021_Immersion.pdf (766.94 kB)

Immersion ultrasonic testing of artificially induced defects in fused filament fabricated steel 316L

Download (766.94 kB)
journal contribution
posted on 2022-10-14, 14:30 authored by Solomon O. Obadimu, John McLaughlin, KYRIAKOS KOUROUSISKYRIAKOS KOUROUSIS
Fused filament fabrication (FFF) with the use of metal-polymer filaments offers a cost-effective solution in additively manufacturing metal parts. Nevertheless, the quality and dimensional characteristics of the FFF produced parts needs to be assured. This short communication reports results and findings from an ongoing investigation on the use of immersion ultrasonic testing (IUT) for the detection of defects in FFF metal parts. In this work, the BASF Ultrafuse 316L material was used with an FFF 3D printer to produce a test specimen for IUT inspection. Two types of artificially induced defects were examined: drilling holes and machining defects. The obtained inspection results are promising in terms of the capability of the IUT method to detect and measure the defects. It was found that the quality of obtained IUT images is not only probe frequency dependent but also sensitive to the part characteristics, indicating a need for a wider range of frequencies and more accurate calibration of the system for this material.

History

Publication

3D Printing and Additive Manufacturing;

Publisher

Mary Ann Liebert

Note

peer-reviewed The full text of this article will not be available in ULIR until the embargo expires on the 08/10/2022

Rights

This is a copy of an article published in 3D Printing and Additive Manufacturing © 2021 copyright Mary Ann Liebert, Inc.3D Printing and Additive Manufacturing is available online at:https://www.liebertpub.com/doi/full/10.1089/3dp.2021.0095

Language

English

Department or School

  • School of Engineering

Usage metrics

    University of Limerick

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC