• Transactions of Materials Processing
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• v.4 no.3
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• pp.214-221
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• 1995

In the paper, we have proposed a new method to determine the initial billet for the forged products using a function approximation in the neural network. The architecture of neural network is a three-layer neural network and the back propagation algorithm is employed to train the network. By utilizing the ability of function approximation of a neural network, an optimal billet is determined by applying the nonlinear mathematical relationship between the aspect ratios in the initial billet and the final products. The amount of incomplete filling in the die is measured by the rigid-plastic finite element method. The neural network is trained with the initial billet aspect ratios and those of the unfilled volumes. After learning, the system is able to predict the filling regions which are exactly the same or slightly different to the results of finite element simulation. This new method is applied to find the optimal billet size for the plane strain rib-web product in cold forging. This would reduce the number of finite element simulation for determining the optimal billet size of forging product, further it is usefully adapted to physical modeling for the forging design.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.2
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• pp.129-135
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• 1997

Experimental studies have been carried out to obtain uniform cups by one operation of backward extrusion. A lot of factors on dimensional accuracy of backward extruded cups are billet material, billet shape, punch shape, punch velocity, geometry of tool, tool material, and lubrication etc. In manufacturing cup-shaped parts by backward extrusion, it is very important to design the initial billet shape or the preform. The objective of this paper is to find that the shape of the initial billet is related to dimensional accuracy and also to manufacture the more accurate product simultaneously reducing the loss of material as forming the shape of the initial billet by means of upsetting.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.217-222
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• 1994

This paper describes the determination of optimal initial billet size for axisymmetric cold forging products using neural networks. The determination of optimal initial billet size is very important in forging design and forming sequence design, because the result of such designs and forming load can be different by variable initial billet sizes. The forming difficulty has been defined as the degree of difficulty in forming by 3 process ' forward extrusion, backward extrusion and upsetting. By neural networks a forming difficulty can be determined with the ratio of shape and forming process. From the graph of maximum, minimum, and average forming difficulties by variable billet sizes, the optimal billet size can be determined. The initial billets of a solid part and a hollow part whichwas determined by this study are compared with the sequence drawing generated by the one of forming sequence design system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.196-200
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• 1996

In general, cylinderical types of billet are use in the backward extrusion. It is difficult to obtain homogenious wall thickness by the backward extrusion using these. It is gradually increased that improving the accuracyand reducing the post machining of the final products. In manufacturing cup shaped parts by backward extrusion, it is very important to design optimal initial billet or preform. These can improve the accuracy of final products and remove the post machining processes. In this study, the influence of final parts geometry by the shape of initial billet as non machined types are discussed.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.11
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• pp.118-124
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• 1995

In the paper, we have proposed a new technique to determine the initial billet for the forged products using a function approximation in neural network. A three-layer neural network is used and a back propagation algorithm is employed to train the network. An optimal billet which satisfied the forming limitation, minimum of incomplete filling in the die cavity, load and energy as well as more uniform distribution of effective strain, is determined by applying the ability of function approximation of the neural network. The amount of incomplete filling in the die, load and forming energy as well as effective strain are measured by the rigid-plastic finite element method. This new technique is applied to find the optimal billet size for the axisymmetric rib-web product in hot forging. This would reduce the number of finite element simulation for determining the optimal billet of forging products, further it is usefully adopted to physical modeling for the forging design

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1994.10a
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• pp.133-140
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• 1994

In this paper, we have proposed a new method to determine the initial billet for the forged products using a function approximation in neural networks. the architecture of neural network is a three-layer neural network and the back propagation algorithm is employed to train the network. By utilizing the ability of function approximation of neural network, an optimal billet is determined by applying nonlinear mathematical relationship between shape ratio in the initial billet and the final products. A volume of incomplete filling in the die is measured by the rigid-plastic finite element method. The neural network is trained with the initial billet shape ratio and that of the un-filled volume. After learning, the system is able to predict the filling region which are exactly the same or slightly different to results of finite element method. It is found that the prediction of the filling shape ratio region can be made successfully and the finite element method results are represented better by the neural network.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.198-203
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• 1995

In the paper, we have proposed a new technique to detemine the initial billet for the forged products using a function approximation in neural network. A three-layer neural network is used and a back propagation algorithm is employed totrain the network. An optimal billet which satisfied the forming limitation, minimum of incomplete filling in the die cavity, load and energyas well as more uniform distribution of effective strain, is determined by applying the ability of function approximation of te neural network. The amount of incomplete filling in the die, load and forming energyas well as effective strain are measured by the rigid-plastic finite element method. The new technique is applied tofind the optimal billet size for the axisymmetric rib-web product in hot forging. This would reduce the number of finite element simulation for determing the optimal billet of forging products, further it is usefully adapted to physical modeling for the forging design.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.8
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• pp.1468-1484
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• 1992

In the present paper forward projection is proposed as a new approach to determine the preform shape in rib-web type forging. In the forward projection technique an optimal billet is determined by applying some mathematical relationship between geometrical trials in the initial billet shape and the final products. In forward projection a volume difference between the desired product shape and the final computed shape obtained by the rigid-plastic finite element method is used as a measure of incomplete filling of working material in the die. At first linear inter-/extrapolation is employed to find a proper trial shape for the initial billet and the method is successfully applied to some cases of different aspect ratios of the initial billet. However, when the initial guesses are not sufficiently near the optimal value linear inter-/extrapolation does not render complete die filling. For more general application, a fuzzy system is used in the forward projection technique in order to determine the initial billet shape for rib-web type forging. It has been thus shown that the fuzzy system is more reliable for the preform design in the rib-web type forging process.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.5
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• pp.134-142
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• 1994

An upper bound elemental technique (UBET) is applied to predict variations of neutral plane and optimal position of the initial billet for rib-wep shaped ring forging. In the analysis, the neutral plane position and velocity fields are determined by minimizing the total power consump- tion with respect to chosen parameters. The degree of die-cavity filling by initial billet-position and the variations of neutral plane by friction condition are investigated. Experiments have been carried out with pure plasticine billets at room temperature. The theoretical predictions of the forging load and the flow pattern are in good agrement with the experimental results.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2004.05a
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• pp.91-94
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• 2004

Preform design techniques have been investigated in efforts to reduce die wear and forming load and to improve material flow, filing ratio, etc. In hot forging processes, a thin deformed part of a workpiece, known as a flash, is formed in the narrow gap between the upper and lower tools. Although designers make tools that generate a flash intentionally in order to improve flow properties, excessive flash increases die wear and forming load. Therefore, it is necessary to make a preform shape that can reduce the excessive flash without changing flow properties. In this paper, a new preform design technique is proposed to reduce the excessive flash in a metal forging process. After a finite element simulation of the process is carried out with an initial billet, the flow of material in the flash region is traced from the final shape to the initial billet. The region belonging to the flash is then easily found in the initial billet. The finite element simulation is then carried out again with the modified billet from which the selected region has been removed. In several iterations of this technique, the optimal preform shape that minimizes the amount of flash without changing the forgeability can be obtained.