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Plastic deformation zone (PDZ) distribution
In the metal plastic forming process, the shape and size of the PDZ has a significant effect on the forming process. Fig. 9 provides the PEEQ (equivalent plastic strain) distribution in the axial section of the cylindrical workpiece in conventional forging. It can be seen that the PDZ is formed firstly in the center part of the cylindrical workpiece, as shown in Fig. 9(b). As the process continues, the PDZ gradually develops toward the rest part and then the whole cylindrical workpiece becomes the PDZ at t=0.615s, as shown in Fig. 9(c). It can be also found from Fig. 9 that the center part of the cylindrical workpiece always has the maximum PEEQ value while the minimum PEEQ value always occurs in the middle part of the upper surface and the lower surface during the conventional forging process. The maximum and minimum PEEQ values are 0.2598 and 0.02165, respectively at the end of the process. If the difference between the maximum and minimum PEEQ is adopted to represent the degree of inhomogeneous deformation of the deformed cylindrical workpiece, the degree of inhomogeneous deformation in conventional forging is 0.23815. In addition, because of the symmetry of geometry and boundary conditions, the PDZ distribu-tion in conventional forging exhibits the symmetry in the axial and radial direction.
The prediction ofmaximum forging load and effective stress
for different material ofbevel gear forging
Abstract
The manufacture of gears by applying hot or cold bulk forming processes is a quite widespread production method due to its well-known basic advantages such as material and time cost reduction and the increased strength of the teeth. However, the associated process planning and tool design are more complicated. In the precision forging of gears, the workpiece volume, the die design, the power requirement and careful processing are more critical than traditional forging technology. For complete filling up, predicting the power requirement is an important feature of the near net-shape forging process. In this paper, a finite element analysis is utilized to investigate the material properties such as yielding stress, strength coefficient and strain hardening exponent effects on forming load and maximum effective stress. The adductive network was then applied to synthesize the data set obtained from the numerical simulation. The predicted results ofthe maximum forging load and maximum equivalent stress of bevel gear forging from the prediction model are consistent with the results obtained from FEM simulation quite well. After employing the prediction model one can provide valuable references in prediction of the maximum forging load and maximum equivalent stress of bevel gear forging under a suitable range of material parameters.
1. Introduction
The precision forging of gears can produce near net-shape forgings with no chipping of their teeth, so it has been used widely in the automobile, astronavigation, etc. The precision of gear forging depends on the precision of the die and its structure, so that the die for precision forging must be designed with consideration of the deformation and the stress state caused by the working pressure and the shrinkage fit. The well-known main advantages of these forgings are: (l) great reduction of material expenses; (2) small machining allowances and closetolerances leading to (3) considerable decrease in machining time and process expenses; and (4) marked increase in strength values due to the favourable microstructure developed in the teeth.
For the forging of bevel gears, the way to complete filling up of the material into a die cavity is regarded as the most important aspect for improving the dimensional accuracy of gears. For complete filling up, predicting the power requirement and improving the dimensional accuracy ofthe gear are an important feature of the forging process. Computer aided engineering (CAE) techniques have been increasingly applied with great success in metal-forming research to predict the forming load, stress and strain distribution. Meidert et aI.[1] proposed two modelingtechniques, [mite element (FE) based numerical modeling and physical modeling with plastic, are being presented as process design tools in cold forging. Mamalis et al. [2] used the implicit FE code MARC and the explicit FE code DYNA 3D to simulate the bevel gear forging process. The simulation results by MARC seem to be in good agreement with the experimental results and, therefore, they enable the forging designer to easily create a CAD/CAM/CAE system for analysing the precision-forging problem successfully. Contrarily, the explicit FE code DYNA 3D seems to fail to simulate the whole problem at a very early stage of the analysis due to structural limitations of the code. Recently, Yang [3] used FEM software DEFORM-3D to simulate the spur gear forging process. The load predicted by the DEFORM-3D is closer to the experimental data than the prediction by Choi et al. [4]. Thus, the DEFORM-3D is appropriate to simulate the forging process of gear. It is necessary to perform a lot of numerical simulations obtain a suitable range of the process or material parameters for producing an acceptable product in metal forming process. Lin and Kwan [5] used the finite element method in conjunction with adductive network to predict an acceptable product of which the minimum wall thickness and the protrusion height fulfil the industrial demand on the T-shape tube hydroforming process. Yang and Hsu [6] used a finite element analysis investigate the maximum forging force and [mal face width under different process parameters such as modules, number of teeth, and the ratio of theheight to diameter of billet. The adductive network is then applied to synthesize the data set obtained from the numerical simulation, and a prediction model is established ultimately.