• English
  • Deutsch
  • Log In
    Password Login
    Research Outputs
    Fundings & Projects
    Researchers
    Institutes
    Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Artikel
  4. Statistical characterization of stress concentrations along butt joint weld seams using deep neural networks
 
  • Details
  • Full
Options
2022
Journal Article
Title

Statistical characterization of stress concentrations along butt joint weld seams using deep neural networks

Abstract
In order to ensure high weld qualities and structural integrity of engineering structures, it is crucial to detect areas of high stress concentrations along weld seams. Traditional inspection methods rely on visual inspection and manual weld geometry measurements. Recent advances in the field of automated measurement techniques allow virtually unrestricted numbers of inspections by laser measurements of weld profiles; however, in order to compare weld qualities of different welding processes and manufacturers, a deeper understanding of statistical distributions of stress concentrations along weld seams is required. Hence, this study presents an approach to statistically characterize different types of butt joint weld seams. For this purpose, an artificial neural network is created from 945 finite element simulations to determine stress concentration factors at butt joints. Besides higher quality of predictions compared to empirical estimation functions, the new approach can directly be applied to all types welded structures, including arc- and laser-welded butt joints, and coupled with all types of 3D-measurement devices. Furthermore, sheet thickness ranging from 1 mm to 100 mm can be assessed.
Author(s)
Braun, Moritz
Institute for Ship Structural Design and Analysis, Hamburg University of Technology
Neuhäusler, Josef
Institute for Material- and Building Research, Munich University of Applied Sciences
Denk, Martin
Engineering Design, Friedrich-Alexander-University Erlangen-Nürnberg
Renken, Finn
Institute for Ship Structural Design and Analysis, Hamburg University of Technology
Kellner, Leon
Institute for Ship Structural Design and Analysis, Hamburg University of Technology
Schubnell, Jan
Fraunhofer-Institut für Werkstoffmechanik IWM  
Jung, Matthias  
Fraunhofer-Institut für Werkstoffmechanik IWM  
Rother, Klemens
Institute for Material- and Building Research, Munich University of Applied Sciences
Ehlers, Sören
Institute for Ship Structural Design and Analysis, Hamburg University of Technology
Journal
Applied Sciences  
Open Access
DOI
10.3390/app12126089
Language
English
Fraunhofer-Institut für Werkstoffmechanik IWM  
Keyword(s)
  • local weld toe geometry

  • weld classification

  • 3-D scans

  • non-destructive testing

  • statistical assessment

  • machine learning

  • fatigue strength

  • stress concentration factor

  • weld quality

  • artificial neural network

  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Contact
© 2024