A Computational Study on Additively Manufactured Welding Electrodes

Authors

  • D.M. Kirkman University of KwaZulu-Natal (UKZN) Author
  • J. Pillay University of KwaZulu-Natal (UKZN) Author
  • J. Padayachee University of KwaZulu-Natal (UKZN) Author

DOI:

https://doi.org/10.17159/2309-8988/2019/v35a5

Keywords:

additive manufacturing, mesh welding machines, copper electrodes

Abstract

Advances in additive manufacturing technology present new design opportunities for metal parts that would otherwise be infeasible with subtractive manufacturing technologies. Clifford Machines & Technology (Pty) Ltd is an international producer of large mesh welding machines. The research was conducted with the aim of investigating the advantages that can be provided through the redesign of the mesh welding electrodes, for production using additive manufacturing. Simulation studies were applied in order to evaluate the performance of the redesigned electrodes and the results were compared to the existing electrodes. The results show that the electrodes designed for additive manufacturing achieved mass reductions of up to 58.2%. The electrodes were also able to support increases of current density by up to 98%, while operating at a lower temperature than the original electrodes. The study has identified the high initial cost of production and increased power consumption to be the disadvantages of additively manufactured electrodes.

Downloads

Download data is not yet available.

Author Biographies

  • D.M. Kirkman, University of KwaZulu-Natal (UKZN)

    Discipline of Mechanical Engineering, University of KwaZulu-Natal (UKZN)

  • J. Pillay, University of KwaZulu-Natal (UKZN)

    Discipline of Mechanical Engineering, University of KwaZulu-Natal (UKZN)

  • J. Padayachee, University of KwaZulu-Natal (UKZN)

    Discipline of Mechanical Engineering, University of KwaZulu-Natal (UKZN)

Downloads

Published

06-12-2019

Issue

Section

Articles

How to Cite

“A Computational Study on Additively Manufactured Welding Electrodes” (2019) R&D Journal, 35, pp. 38–46. doi:10.17159/2309-8988/2019/v35a5.

Most read articles by the same author(s)