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 of the 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.

This study applies the finite element method (FEM) in conjunction with an nanoindentation test to predict the loading curve and stress distribution of thin hard coatings. To verify the prediction of FEM simulation for loading and unloading process, the experimental data are compared with the results of current simulation. Loading curve is investigated for different material parameters, such as elastic modulus E, yield stress Y0 and tangent modulus ET of nanoindentation process, by finite element analysis. The effects of material properties of thin film on the stress distribution for loading and unloading in the nanoindentation are also investigated. Therefore, the loading curve and stress distribution will be prediction for the different material parameters of nanoindentation process.

No abstract

In this study, the predictive model of friction coefficient using cylindrical compression was constructed through combining the finite element method and neutral networks. Namely, the related data of the materials characters, cylinder compression bulging, and how they were associated with friction coefficient was obtained by the finite element method. Based on those analysis data, the relationship model, reflecting the relationship among the materials characters such as strength coefficient and strain-hardening exponent, the compression bulging such as reduction height, expanding in upper ending, expanding in bottom ending, maximum expanding in outside diameter and the friction coefficient in workpiece/die interface, was constructed. Finally, the cross verification between finite element analysis, prediction by neutral network model and the experiments of cylindrical compression testing and ring compression testing are repeatedly checked to ensure the accuracy and reliability of the constructed model. Results of the current study indicate that their errors are extremely limited, and the developed predictive system is reliable and feasible.