• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.5
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• pp.727-734
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• 1997

In practice, the design of forging processes is performed based on an experience-oriented technology, that is designer's experience and expensive trial and errors. Using the finite element simulation and the artificial neural network, we propose an optimal die geometry satisfying the design conditions of final product. A three-layer neural network is used and the back propagation algorithm is employed to train the network. An optimal die geometry that satisfied the same between inner extruded rib and outer extruded one is determined by applying the ability of function approximation of neural network. The neural networks may reduce the number of finite element simulation for determine the optimal die geometry of forging products and further they are usefully applied to physical modelling for the forging design.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7754-7760
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• 2015

The outer tie rod is one of the part of steering system, the optimization process was executed to find the lightweight design. The inner tie rod was considered in the optimum design of an outer tie rod. it could be closer to the test condition than in the case of considering outer tie rod only. The aluminum forging material was considered as a weight reduction proposal. The target of optimization was the shape of the minimum weight to resist at the load of buckling. RSM and Kriging interpolation method were applied as a optimization method to consider the nonlinear shape optimization problem. Then, 16.3%, 16.6% of weight reduction was obtained from the result comparing with that of the initial model. The results of meta model optimization was compared with that of finite element method. The error values of buckling load estimation were 2.6%, 2.04%. and those of weight estimation were 0.17%, 0.13%. Therefore, it seemed that the result of Kriging model could be obtained closer to optimum value than that of RSM model.