Parameter identification of superplastic constitutive model by GA-based global optimization method

Abstract

Abstract The successful application of superplastic constitutive model considering grain growth depends on how well the material parameters are identified. However, it is difficult to obtain satisfactory parameters by using the traditional parameter identification method. The reasons are due to that it is difficult to separate one physical process from other physical processes involved in the model. In this paper, the material parameters are identified by inverse analysis. The objective function is first provided, and then a hybrid optimization method has been developed and used to identify the parameters. The developed optimization method incorporates the strengths of GA, the Levenberg–Marquardt algorithm and the augmented Gauss–Newton algorithm. The difficulty of choosing appropriate initial values for the parameters in the traditional optimization technique is overcome by applying the GA and the shortcoming of the slow convergent speed of the GA is surmounted by applying the Levenberg–Marquardt algorithm and the augmented Gauss–Newton algorithm. At last, taking Ti–6Al–4V as an example, a set of satisfactory material parameters is obtained. The calculated results agree with the experimental results well.