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Liverpool John Moores University

Parametric Studies Based Mechanical and Thermal Modelling of Spot Welded Joints

Abstract

dc:description.abstract

This work has focused on formulating a experimental/numerical framework for the investigation of spot weld properties and performance. An Inverse temperature measurement approach has been established to predict the thermal history of a spot welded joints using remote thermocouples. This method incorporated the experimental data into an Artificial Neural Network (AAN) to predict cooling curves of the HAZ. Advanced modelling programs have been developed to simulate spot welded joints and thermocouples. Using the programs to investigate the effects of the key dimensional or material parameters on the mechanical or thermal response of spot welded joints of steels and different thermocouple joints relevant to their applications. Graphical User Interface Abaqus plug-ins of spot welded joints have developed using Python scripting and are used to investigate the effect of nugget size and sheet thickness on the stress and deformation of spot welded joints of steel. These works are important to establish an integrated approach to study the electrical, mechanical and thermal process of the spot welding process.

Degree

thesis:*
Name dc:type.qualificationname
phd
Level dc:type.qualificationlevel
doctoral
Grantor dc:publisher.institution
Liverpool John Moores University
Year dc:date.issued
2017

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Norbury, AAW
Contributors dc:contributor
  • Ren, X
  • Rothwell, G

Subjects

dc:subject × 1

Identifiers

dc:identifier.*
OAI identifier oai:identifier
oai:researchonline.ljmu.ac.uk:5848

Chain of custody

source
Harvested from
Liverpool Jon Moores University
Base URL
researchonline.ljmu.ac.uk/cgi/oai2
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Norbury, AAW. Parametric Studies Based Mechanical and Thermal Modelling of Spot Welded Joints. doctoral thesis, Liverpool John Moores University, 2017. https://doi.org/10.24377/LJMU.t.00005848