elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Statistical Characterization of Stress Concentrations along Butt Joint Weld Seams Using Deep Neural Networks

Braun, Moritz and Neuhäusler, Josef and Denk, Martin and Renken, Finn and Kellner, Leon and Schubnell, Jan and Jung, Matthias and Rother, Klemens and Ehlers, Sören (2022) Statistical Characterization of Stress Concentrations along Butt Joint Weld Seams Using Deep Neural Networks. Applied Sciences, 12, pp. 1-17. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/app12126089. ISSN 2076-3417.

[img] PDF - Published version
3MB

Official URL: https://www.mdpi.com/2076-3417/12/12/6089

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.

Item URL in elib:https://elib.dlr.de/187551/
Document Type:Article
Title:Statistical Characterization of Stress Concentrations along Butt Joint Weld Seams Using Deep Neural Networks
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Braun, MoritzHamburg University of Technology, Institute of Ship Structural Design and Analysishttps://orcid.org/0000-0001-9266-1698UNSPECIFIED
Neuhäusler, JosefUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Denk, MartinUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Renken, FinnUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Kellner, LeonUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Schubnell, JanFraunhofer Institute for Mechanics of MaterialsUNSPECIFIEDUNSPECIFIED
Jung, MatthiasUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Rother, KlemensUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Ehlers, SörenUNSPECIFIEDhttps://orcid.org/0000-0001-5698-9354UNSPECIFIED
Date:15 June 2022
Journal or Publication Title:Applied Sciences
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:12
DOI:10.3390/app12126089
Page Range:pp. 1-17
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2076-3417
Status:Published
Keywords: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
HGF - Research field:Energy
HGF - Program:other
HGF - Program Themes:E - no assignment
DLR - Research area:Energy
DLR - Program:E - no assignment
DLR - Research theme (Project):E - no assignment
Location: Geesthacht
Institutes and Institutions:Institute of Maritime Energy Systems
Deposited By: Piazza, Hilke Charlotte
Deposited On:17 Oct 2022 07:24
Last Modified:02 Dec 2022 09:24

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Website and database design: Copyright © German Aerospace Center (DLR). All rights reserved.