摘要
The accuracy of numerical simulations and many other material design calculations, such as the rolling force, rollingtorque, etc., depends on the description of stress-strain relationship of the deformed materials. One common methodof describing the stress-strain relationship is using constitutive equations, with the unknown parameters fitted byexperimental data obtained via plane strain compression (PSC). Due to the highly nonlinear behaviour of the constitutive equations and the noise included in the PSC data, determination of the model parameters is difficult. Inthis paper, genetic algorithms were exploited to optimise parameters for the constitutive equations based on thePSC data. The original PSC data were processed to generate the stress-strain data, and data pre-processing wascarried out to remove the noise contained in the original PSC data. Several genetic optimisation schemes have beeninvestigated, with different coding schemes and different genetic operators for selection, crossover and mutation.It was found that the real value coded genetic algorithms converged much faster and were more efficient for theparameter optimisation problem.
