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.

[1]  E. L. Cardoso,et al.  Parameter Identification of Damage Models Using Genetic Algorithms , 2010 .

[2]  K. Liang,et al.  The comparison of Qian-Liu model and Anand model for uniaxial tensile test of SAC305 , 2019, 2019 20th International Conference on Electronic Packaging Technology(ICEPT).

[3]  Jürgen Wilde,et al.  Viscoplastic Anand model for solder alloys and its application , 2000 .

[4]  P. Lall,et al.  Determination of Anand constants for SAC solders using stress-strain or creep data , 2012, 13th InterSociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems.

[5]  Luciano Lamberti,et al.  A general framework for identification of hyper-elastic membranes with moiré techniques and multi-point simulated annealing , 2008 .

[6]  L. Anand.  Constitutive equations for hot-working of metals , 1985 .

[7]  Xu Chen,et al.  Applying Anand model to low-temperature sintered nanoscale silver paste chip attachment , 2009 .

[8]  J. Wilde,et al.  Applying Anand Model to Represent the Viscoplastic Deformation Behavior of Solder Alloys , 2001 .

[9]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[10]  Amitava Chatterjee,et al.  A hybrid cooperative-comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding , 2008, Expert Syst. Appl..

[11]  M. Thoben,et al.  Rate dependent constitutive relations based on Anand model for 92.5Pb5Sn2.5Ag solder , 2000 .

[12]  L. Anand,et al.  An internal variable constitutive model for hot working of metals , 1989 .

[13]  Xiuxia Sun,et al.  Particle Swarm Optimization for Route Planning of Unmanned Aerial Vehicles , 2006, 2006 IEEE International Conference on Information Acquisition.

[14]  Hossein Amirabadi,et al.  Application of artificial neural network and optimization algorithms for optimizing surface roughness, tool life and cutting forces in turning operation , 2013 .