Abstract
In this study, three parameter optimization methods and two designs of experiments (DOE) were used for the optimization of three major design parameters ((bill diameter (D), billet length (L), and barrier wall design (BWD)) in crown forging to improve the formability of aluminum workpiece for shock absorbers. The first optimization method is the response surface method (RSM) combined with Box-Behnken's experimental design to establish fifteen (15) sets of parameter combinations for research. The second one is the main effects plot method (MEP). The third one is the multiobjective optimization method combined with Taguchi's experimental design method, which designed nine (9) parameter combinations and conducted research and analysis through grey relational analysis (GRA). Initially, a new type of forging die and billet in the controlled deformation zone (CDZ) was established by CAD (computer-aided design) modeling and the finite element method (FEM) for model simulation. Then, this investigation showed that the optimal parameter conditions obtained by these three optimization approaches (RSM, MEP, and multiobjective optimization) are consistent, with the same results. The best optimization parameters are the dimension of the billet ((D: 40 mm, the length of the billet (L): 205 mm, and the design of the barrier wall (BWD): 22 mm)). The results indicate that the optimization methods used in this research all have a high degree of accuracy. According to the research results of grey relational analysis (GRA), the size of the barrier wall design (BWD) in the controllable deformation zone (CDZ) has the greatest influence on the improvement of the preforming die, indicating that it is an important factor to increase the filling rate of aluminum crown forgings. At the end, the optimized parameters are verified by FEM simulation analysis and actual production validation as well as grain streamline distribution, processing map, and microstructure analysis on crown forgings. The novelty of this work is that it provides a novel preforming die through the mutual verification of different optimization methods to solve a typical problem such as material underfill.
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