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. On...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.展开更多
This work deals with a reliability assessment of springback problem during the sheet metal forming process. The effects of operative parameters and material properties, blank holder force and plastic prestrain, on spr...This work deals with a reliability assessment of springback problem during the sheet metal forming process. The effects of operative parameters and material properties, blank holder force and plastic prestrain, on springback are in- vestigated. A generic reliability approach was developed to control springback. Subsequently, the Monte Carlo simula- tion technique in conjunction with the Latin hypercube sam- pling method was adopted to study the probabilistic spring- back. Finite element method based on implicit/explicit al- gorithms was used to model the springback problem. The proposed constitutive law for sheet metal takes into account the adaptation of plastic parameters of the hardening law for each prestrain level considered. Rackwitz-Fiessler al- gorithm is used to find reliability properties from response surfaces of chosen springback geometrical parameters. The obtained results were analyzed using a multi-state limit reli- ability functions based on geometry compensations.展开更多
文摘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.
文摘This work deals with a reliability assessment of springback problem during the sheet metal forming process. The effects of operative parameters and material properties, blank holder force and plastic prestrain, on springback are in- vestigated. A generic reliability approach was developed to control springback. Subsequently, the Monte Carlo simula- tion technique in conjunction with the Latin hypercube sam- pling method was adopted to study the probabilistic spring- back. Finite element method based on implicit/explicit al- gorithms was used to model the springback problem. The proposed constitutive law for sheet metal takes into account the adaptation of plastic parameters of the hardening law for each prestrain level considered. Rackwitz-Fiessler al- gorithm is used to find reliability properties from response surfaces of chosen springback geometrical parameters. The obtained results were analyzed using a multi-state limit reli- ability functions based on geometry compensations.
