An experimental design of spot welding of Ti6Al4V sheets and numerical modeling approach


Bozkurt F., ÇAKIR F. H.

WELDING IN THE WORLD, cilt.65, sa.5, ss.885-898, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 65 Sayı: 5
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1007/s40194-020-01054-3
  • Dergi Adı: WELDING IN THE WORLD
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex
  • Sayfa Sayıları: ss.885-898
  • Anahtar Kelimeler: Ti6Al4V, Spot welding, Taguchi, Numerical modeling, Tensile shear strength, FINITE-ELEMENT-ANALYSIS, MECHANICAL-PROPERTIES, STRENGTH, MICROSTRUCTURE, OPTIMIZATION, PARAMETERS, ELECTRODE, JOINTS, STEEL, TIME
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

In this study, the traditional alpha-beta titanium alloy Ti6Al4V sheets were joined by the resistance spot welding (RSW) process. RSW method was modeled numerically and investigated experimentally by using different welding parameters. In numerical modeling, material properties and process parameters were modeled with the finite element method (FEM), and simulations were realized in experimentally performed parameters. In order to validate and calibrate the developed numerical model, preliminary experiments were carried out and then Taguchi L9 orthogonal experimental design was determined to examine the effect of different parameters that are thought to be suitable for this process. The signal/noise (S/N) ratio was used to interpret the results in the determined experimental setup. The experimental study aims to validate the established numerical model and to obtain information such as the unpredictable strength of the welded joint through the numerical model. The experimental results were analyzed by using the Taguchi technique with the help of the Minitab software. In this study, electrode force, welding current, and welding time parameters were determined as the control factors, and the effect of these factors on the results was found using analysis of variance (ANOVA). Lastly, verification tests were performed, and it was determined that optimization was applied successfully.