Title: Neural Network and Genetic Algorithm Based Finite Element Model for Optimal Die Shape Design in Al-1100 Cold Forward Extrusion
Abstract:This paper employs rigid-plastic finite element DEFORMTM 3D software to estimate the plastic deformation behavior of an aluminum billet during its axisymmetric extrusion through a conical die.The die ...This paper employs rigid-plastic finite element DEFORMTM 3D software to estimate the plastic deformation behavior of an aluminum billet during its axisymmetric extrusion through a conical die.The die and container are assumed to be rigid bodies and the temperature change induced during extrusion is ignored.The important parameters which effect on the extrusion process were assumed to be: the reduction of area (0.75), semi-cone die angles (5,6,7,8,10,12, and 14 o ) coefficient of friction is 0.05 and the extrusion speed is 250 mm/s.Under various extrusion conditions, the present numerical analysis estimates the stresses, the die load and the flow velocity of the billet at the die exit.Genetic algorithm coupled with neural network is employed to find optimum die angle leading to minimum stresses without any constraint.The simulation results confirm the suitability of the current finite element software for modeling the three-dimensional cold extrusion of aluminum rod.Read More