Abstract
In order to control the precision forging forming quality and improve the service life of die, a multiobjective optimization method for process parameters design was presented by applying Latin hypercube design method and response surface model approach. Meanwhile the deformation homogeneity and material damage of forging parts were proposed for evaluating the forming quality. The forming load of die was proposed for evaluating the service life of die. Then as a case of study, the radial precision forging for a hollow shaft with variable cross section and wall thickness was carried out. The 3D rigid-plastic finite element (FE) model of the hollow shaft radial precision forging was established. The multiobjective optimization forecast model was established by adopting finite element results and response surface methodology. Nondominated sorting genetic algorithm-II (NSGA-II) was adopted to obtain the Pareto-optimal solutions. A compromise solution was selected from the Pareto solutions by using the mapping method. In the finite element study on the forming quality of forging parts and the service life of dies by multiobjective optimization process parameters, the feasibility of the multiobjective optimization method presented by this work was verified.
Highlights
Precision forging technology is an advanced technology in recent years; it has important significance for improving mechanics properties of forming and energy saving
The present study is aimed at developing an approach to optimizing the process parameters with the combination of Response surface methodology (RSM) and FEM to improve the precision forging parts quality and the service life of die
Kalyanmoy Deb’s nondominated sorting genetic algorithm-II (NSGA-II) is an improved multiobjective evolutionary algorithm based on NSGA; it can keep the diversity without specifying any additional parameters
Summary
Precision forging technology is an advanced technology in recent years; it has important significance for improving mechanics properties of forming and energy saving. Optimum was achieved by using pattern search optimization method to search response surface describing relationship between preform shape and die cavity fill ratio. Oudjene et al [9] presented a response surface methodology, based on moving least-square approximation and adaptive moving region of interest for shape optimization problem. The geometries of both the punch and the die are optimized to improve the joints resistance to tensile loading. The present study is aimed at developing an approach to optimizing the process parameters with the combination of RSM and FEM to improve the precision forging parts quality and the service life of die. The details of optimizing process parameters are presented with a variable cross section and thickness drive shaft radial precision forging as an example
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