Research On Failure Mechanisms And Life Prediction On The Hot Forging Die

The growing global concern to increase productivity,quality and decrease the final cost over the forging line has led to the study of the unavoidable factors.In the forming process,special hot forging,the tool(die)is one factor that draws scientists’ attention.The service life of the die has a distinguished effect on the quality and cost of the final product.The die costs constitute 50%the of production cost when considering the replacement time of tools and unexpected damages.To prolong the service life of die in the first step dependent on analysis and monitoring the progress of the most effective failure mechanisms.In this light,different destructive and non-destructive methods have been applied.Recently optical scanners,as a noncontact method due to high resistance to disturbance and harsh conditions such as high temperature,have been employed for this issue.However,due to its high cost and not being readily available,it has no wide application in the forging process.This research investigated the degradation mechanisms of a hot-forging die made of H21 steel.Three dies with different numbers of the forgings from the same production line with the material have been selected for analysis.An optical scanning method,finite element analysis(FEA),scanning electron microscopy(SEM),metallography,nanoindentation,electron backscattering diffraction(EBSD),transmission electron microscopy(TEM),and an artificial neural network(ANN)model was employed for analyzing the progress of main degradation mechanisms and corresponding the predicting the service life of the die.An optical scanner was applied to analyze the amount of wear(material loss)and geometric deviation.The ploy works cooperated with the optical scanner to analyze the point cloud obtained by the scanner.Finite element analysis(FEA)is used to obtain the process parameters.To analyze the common failure mechanisms,namely,plastic deformation,mechanical fatigue cracking,and thermal cracking,scanning electron microscopy(SEM)and metallography was applied.Due to the possibility of the occurrence of different failure mechanisms simultaneously in the same region,electron backscattering diffraction(EBSD),nanoindentation,and transmission electron microscopy(TEM)has been applied to characterize the microstructure and mechanical properties of deformed regions.In the end,an artificial neural network(ANN)model was applied to predict the common failure mechanisms and the corresponding prediction of the service life in hot forging dies.A neural network model by different configurations is implemented through the MATLAB code and neuro solutions software to obtain the most excellent structure,topology,and inner configuration.The obtained results indicated that the amount of wear and deformation in some regions of the forging die is considerable.The amount of strain,strain rate,and temperature were reasonable for the various types of softening mechanisms,such as the formation of the blocky ferrite phase,recovery of dislocations,tempering,and dynamic recrystallization.In addition,dynamic recrystallization happened by both the continuous and discontinuous mechanisms causing the formation of fine and equiaxed grain with a low density of dislocations.On the other hand,in these regions,the maximum wear identified by the scanner showed a good agreement between the scanning technique and microstructure analysis results.By relying on the capability of ANNs,the possibility of prediction of main failures such as: abrasive wear,plastic deformation,mechanical fatigue cracking,and thermal cracking based on the actual experimental data has been provided.The relevant results confirmed the capability of the present method to apply as an appropriate tool for predicting the service life of die in the hot forging process.In the end,the obtained results showed the models’ advantage in predicting the optimal outputs.In this research,the proposed non-contact method without the assistance of a measurement arm can be the distinguished step in applying this technique for inspection and leads to reducing the scanning equipment,and time and corresponding analysis cost.This method allows analyzing the forging die without dismounting the tools from the forging unit.However,because the hot forging die is exposed to over 500°C and expansion,this may bring some errors in the measuring results.On the other hand,the model proposed in this study can predict the unexpected common failure mechanisms,which reduces the final cost by up to 15%.In addition,the results of this study can help scientists and engineers understand the origin of the failure and the most critical softening mechanisms in deformed die regions during the hot forging process.Since this research builds based on the data from the hot forging production line,it is expected to bring distinguished financial benefits by the effect on the final cost.