# Intelligent Optimization of Process Parameters in Limit Gauge Hot Strip Rolling
## 5 Optimization of Process Parameters and Result Analysis  
### Optimization of Process Parameters  
Taking the rolling rhythm, intermediate slab thickness, final rolling temperature, in furnace time and rolling sequence in roll change cycle as recommended processes, taking the rolling stability score as the goal, the neural network model as the fitness function for global optimization. The genetic algorithm is used to search the trained neural network model, and the mapping relationship is optimized with the rolling stability score of 100 as the goal, until the rolling process suggestions that meet the conditions are found. The extreme specification rolling model constructed has the characteristics of many optimization parameters and high complexity. There are slight differences in the results of multiple optimizations, but all of them can achieve the simulation effect of 100 points of rolling stability. In field applications, the rolling rhythm requirements must be met while ensuring rolling stability. Therefore, the optimal process parameter combination obtained by optimization in the shortest time is used as the rolling recommendation. In order to ensure production safety, the rolling recommendation will be determined by the field technology. Personnel decides whether to accept or not.  
The strip steel MRTRG00201 with width of 1276 mm in a steel plant is taken as the test object. There are 91 thin gauge samples with thickness less than 4 mm in the rolling history samples of strip steel with the same equipment status and product specification, of which the minimum thickness is 3.2 mm, the maximum thickness is 4 mm, the highest rolling stability score is 100 and the lowest is 80. However, the 3 mm thick strip has not been rolled. When the 3 mm thick strip needs to be rolled on site, there is no rolling history sample as a reference, and there is no way to obtain the setting of various process values and rolling stability. Therefore, taking the 3 mm thick strip as the test object, the rolling process parameters are optimized through the limit specification rolling model combined with neural network and genetic algorithm, and the optimal process values are proposed.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Process Items** & **Minimum** & **Maximum** & **Units** \\ \hline
1 & Mill Pacing & 6.44 & 33.25 & (/h) \\
2 & Thickness of & 43.26 & 44.17 & (mm) \\
3 & Intermediate Billet & 1091.97 & 1148.37 & (\({}^{\circ}\)C) \\
4 & Time in the Furnace & 142.24 & 217.38 & (min) \\
5 & Roll Change Order & 1.98 & 156.55 & - \\ \hline \end{tabular}
\end{table}
Table 6: Model optimization process range.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Process Items** & **Minimum** & **Maximum** & **Units** \\ \hline
1 & Mill Pacing & 6.5 & 32.92 & (/h) \\
2 & Thickness of & 43.70 & 43.73 & (mm) \\
3 & Intermediate Billet & 1103 & 1137 & (\({}^{\circ}\)C) \\
4 & Time in the Furnace & 143.68 & 215.23 & (min) \\
5 & Roll Change Order & 2 & 155 & - \\ \hline \end{tabular}
\end{table}
Table 5: Excellent sample set process range.  
In the iteration process, the iteration operation of the crossover selection mutation crossover selection cycle is repeated [29; 30], and the final optimization objective function value tends to be stable, and the individual with the highest rolling stability is output. The optimization results are as follows: the rolling rhythm is 30.86/h, the thickness of intermediate slab is 43.71 mm, the final rolling temperature is 1100.12 degC, the furnace time is 150.16 min, the serial number is 30.36 in the roll change cycle, and the rolling stability score is 100 points, as shown in Table 7: