Parameter identification of Anand constitutive models for SAC305 using the intelligent optimization algorithm

Through finite element simulation to obtain the reliability of electronic products is an effective way to reduce experiment cost and shorten the development period for electronic industry. The accurate identification of the parameters of the constitutive model is a prerequisite for finite element simulation. One of the most widely employed constitutive model for describing the mechanical properties of solder alloys is the Anand model, which are determined by nine material parameters. The identification method of the nine material parameters of the Anand model is by gradient descent method, which is sensitive to the value of the initial values. An effective approach to solve this problem is employing intelligent algorithm to obtain the nine parameters of Anand model. The Particle Swarm Optimization (PSO), which is a parallel intelligent algorithm based on evolving, has the advantages of fast search speed, high efficiency and easy to implement. In this work, through gradient descent method and PSO, we obtain the Anand constitutive model parameters of SAC305 from a set of uniaxial tensile tests of SAC305 performed under different constant strain rates and different constant test temperatures. Compared with the gradient descent method, the PSO algorithm is an effective method to obtain the Anand parameters of SAC 305. The PSO algorithm shows a great application in determining the constitutive model parameters.