## 3 Orthogonal Experiments and Grey Relational Analysis
### Neural Network Genetic Algorithm Function Optimization
#### 4.3.3 Result Analysis Verification  
Based on the neural network genetic algorithm function optimization model, the results obtained are shown in Table 8. When the blank holder force reached 462.71 kN and the die clearance reached 1.09 mm, the score of the comprehensive score approached 0.03, indicating the optimal \"maximum thickening rate\" and \"maximum thinning rate\" were 3.97% and 25.96%, respectively, and the forming quality of the part was the best.  
In order to verify the accuracy of the optimization model and the reliability of the analysis results, DYNAFORM finite element software was used to perform numerical simulation on the optimal process parameter combination, and the simulation analysis results are shown in Figure 8.  
As shown in Figure 8, the overall forming effect of the part was good. Although there was a large area of slight wrinkling in the part, the numerical indicators met the quality requirements, and there was no cracking; the \"maximum thickening rate\" was 3.32%, meeting the production requirement that the \"maximum thickening rate\" did not exceed 5%; the \"maximum thinning rate\" was 25.26%, meeting the production requirement that the \"maximum thinning rate\" was less than 30%. Subsequently, the \"maximum thinning rate\" optimized was compared with the \"maximum thinning rate\" simulated, as shown in Table 9.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Process Parameter** & **Value** & **Maximum Thickening Rate/\%** & **Maximum Thinning Rate/\%** \\ \hline Blank Holder Force/kN & 462.71 kN & & \\ Stamping Speed/mm\(\cdot\)s\({}^{-1}\) & 3000 mm\(\cdot\)s\({}^{-1}\) (confirmed) & & \\ Friction Coefficient & 0.125 (confirmed) & & \\ Die Clearance/mm & 1.09 mm & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Optimization result.  
Figure 8: Simulation analysis cloud diagram: (**a**) forming limit diagram; (**b**) thickness distribution diagram.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**“Maximum Thickening Rate” for Optimization** & **“Maximum Thickening Rate” for Simulation Analysis/\%** & **“Maximum Thinning Rate” for Optimization** & **“Maximum Thinning Rate” for Simulation Analysis/\%** & **Average Error Value/\%** \\ \hline
3.97\% & 3.32\% & 25.96\% & 25.26\% & 0.675\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Optimization error table.  
The difference between the optimization results and the simulation results was extremely small, with an average error value of only 0.675%, and the forming effect of the part was greatly improved. This result demonstrates the excellent predictive accuracy and optimization efficiency of the neural network genetic algorithm function optimization model.  
In addition, by comparing the results of range analysis and the simulation effects of the optimization model, the performance differences between the two methods in improving the metal sheet forming process are revealed. The \"maximum thickening rate\" and \"maximum thinning rate\" obtained from range analysis were 4.53% and 28.21%, respectively, while after applying the optimization model, the \"maximum thickening rate\" and \"maximum thinning rate\" were reduced to 3.32% and 25.26%, respectively, showing reductions of 1.21% and 2.95%. Although the orthogonal experiment also had a certain effect on improving the quality of metal sheet forming, its optimization effect was relatively weaker compared with the optimization model.