University of Illinois Urbana-Champaign

Machine learning-based non-destructive evaluation of fatigue damage in metals

Hsu, Min-Hsiu

Content Files
HSU-THESIS-2021.pdf

Permalink

https://hdl.handle.net/2142/110539

Description

Title
Machine learning-based non-destructive evaluation of fatigue damage in metals
Author(s)
Hsu, Min-Hsiu
Issue Date
2021-04-29
Director of Research (if dissertation) or Advisor (if thesis)
Shao, Chenhui
Department of Study
Mechanical Sci & Engineering
Discipline
Mechanical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2021-09-17T01:11:12Z
Keyword(s)
  • non destructive testing
  • non destructive evaluation
  • metal fatigue
  • machine learning
  • remaining useful life
  • remanufacturing
  • sensor fusion
  • linear ultrasound
  • nonlinear ultrasound
  • residual stress
  • full width at half maximum
Abstract
Non-destructive evaluation (NDE) of fatigue damage in metals is crucial for ensuring high product performance and safety. In remanufacturing, NDE for the incoming recycled metal materials is also essential to maximize the benefits of utilizing such materials. However, critical challenges exist in the development of NDE techniques for used components: an individual NDE technology is only sensitive to specific fatigue conditions; and analytics methods are lacking for quantitatively measuring accumulated mechanical damage and conducting prognostics in an early fatigue stage. In this thesis, we propose a novel machine learning-based NDE technology by combining the strengths of linear ultrasonic (LU) and nonlinear ultrasonic (NLU) methods to characterize material properties and flaws at multiple length scales. Besides, a remaining useful life (RUL) estimation framework with hierarchical classifiers and S-N curves for identifying fatigue damage levels and inferring RUL is developed. In addition, regression models are developed to estimate residual stress and full width at half maximum (FWHM). The effectiveness of the proposed methods is demonstrated by using life cycle fatigue testing data for 5052-H32 aluminum alloy.
Graduation Semester
2021-05
Type of Resource
Thesis
Permalink
http://hdl.handle.net/2142/110539
Copyright and License Information
Copyright 2021 Min-Hsiu Hsu

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Dissertations and Theses - Mechanical Science and Engineering