A Comparison Between Hardness-Scaling and Ball-Indentation Techniques on Predicting Stress/Strain Distribution and Failure Behavior of Resistance Spot Welded Advanced High Strength Steel Journal Articles uri icon

  •  
  • Overview
  •  
  • Research
  •  
  • Identity
  •  
  • Additional Document Info
  •  
  • View All
  •  

abstract

  • Abstract To accurately model the resistance spot welding (RSW) joint in finite element analysis (FEA), the constitutive behavior of materials in various weld regions such as heat-affected zone (HAZ) should be measured. Due to the sharp temperature gradient through RSW specimens, microstructural and corresponding mechanical properties of weld regions are different. Additionally, the size of RSW is small; hence, it is challenging to directly measure the stress–strain curve of materials. In this regard, hardness-scaling and ball-indentation techniques are among the popular methods to in-directly measure the stress–strain curve of these materials. However, the effectiveness of these two techniques on predicting the stress/strain distribution and failure behavior of resistance spot welded advanced high strength steels (AHSS) is not clear. In the present work, the stress–strain curves obtained through hardness-scaling and ball-indentation techniques have been compared. The stress/strain distribution and failure behavior of the resistance spot welded AHSS specimen have been simulated by the stress–strain data obtained using the two methods. The simulation results have been compared with experimental analysis. The results showed that both methods can accurately predict the failure location. With the comparison of FEA results with experiment analysis, it was shown that the ball-indentation method provides slightly better predictions of failure behavior compared to the hardness-scaling method. However, the harness scaling method is a simple and convenient technique, which can be implemented as a qualitative analysis for the failure behavior of RSW joints.

authors

  • Zhang, Shiping
  • Ghatei-Kalashami, Ali
  • Midawi, Abdelbaset RH
  • Zhou, Norman

publication date

  • August 1, 2022