Optimization of process parameters of High Pressure Die Casting process
High-pressure die casting is a versatile process for producing engineered metal parts. There are many attributes involved which contribute to the complexity of the process. It is essential for the engineers to optimize the process parameters and improve the surface quality. However, the process parameters are interdependent and in conflict in a complicated way, and optimization of the combination of processes is time-consuming. In this work, an evaluation system for the surface defect of casting has been established to quantify surface defects, an artificial neural network was introduced to generalize the correlation between surface defects and die casting parameters, such as mold temperature, pouring temperature, and injection velocity. It was found that the trained network has great forecast ability. Furthermore, the trained neural network was employed as an objective function to optimize the processes. The optimal parameters were employed, and an acceptable surface quality for castings was achieved.
Dies for aluminum alloys die-casting fail because of a great number of a different and simultaneously operating factors. Die design, material selection, and thermal stress fatigue due to the cyclic working process, as well as to low and inhomogeneous initial die temperature contribute to the failures and cracks formation on/in dies. In the frame of the presented work the intensity and homogeneity of the temperature fields on the working surface of the testing die were checked through thermographic measurements, and failures and cracks on the working surface of the die were analyzed by the non-destructive metallographic examination methods.
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