## 2 Introduction to the Steel Plate Cold Straightening Auxiliary Decision-Making Data
### Multi-Machine Competition and Integration Modules
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**N** & **Train** & **Test** & **Models** & **Good** & **Defect** \\ \hline \multirow{4}{*}{2} & & & & \(-7.69139+0.017657\times X1-\) & & \\  & & & \(0.032719\times X2-18.797067\times\) & & \\  & & & \(X3+0.001265\times X4+\) & & \\  & & & \(0.029409\times X5+0.00221\times\) & & \\  & & & \(X6+0.048697\times X2-\) & & \\  & & & \(5.854059\times X8+3.87\times X9\) & & \\  & & & \(-7.39+0.02057\times X1-\) & & \\
3 & \(1/2-3/26\) & \(4/2-4/3\) & \(0.001469\times X4+0.03021\times X5+\) & & \\  & & & \(0.003151\times X6+0.05464\times\) & & \\  & & & \(X7-6.939\times X8+3.78\times X9\) & & \\  & & & \(-7.05+0.02081\times X1-\) & & \\  & & & \(0.04193\times X2-18.31\times X3+\) & & \\
4 & \(1/2-4/3\) & \(4/4-4/10\) & \(0.001401\times X4+0.0294\times X5+\) & 100\% & (100\%) \\  & & & \(0.003752\times X6+0.05509\times\) & & \\  & & & \(X7-7.088\times X8+4.162\times X9\) & & \\  & & & \(-7.06+0.02058\times X1-\) & & \\
5 & \(1/2-4/10\) & \(4/11-4/15\) & \(0.001393\times X4+0.02949\times X5+\) & 100\% & (100\%) \\  & & & \(0.00374\times X6+0.05492\times\) & & \\  & & & \(X7-7.035\times X8+4.083\times X9\) & & \\  & & & \(-6.98+0.02051\times X1-\) & & \\  & & & \(0.04155\times X2-18.56\times X3+\) & & \\
6 & \(1/2-4/15\) & \(4/18-4/24\) & \(0.001394\times X4+0.02962\times X5+\) & 100\% & (100\%) \\  & & & \(0.003759\times X6+0.05556\times\) & & \\  & & & \(X7-7.135\times X8+3.92\times X9\) & & \\  & & & \(-7.07+0.02024\times X1-\) & & \\  & & & \(0.04141\times X2-18.62\times X3+\) & & \\
7 & \(1/2-4/24\) & \(4/25-4/30\) & \(0.001404\times X4+0.02998\times X5+\) & 100\% & (100\%) \\  & & & \(0.003811\times X6+0.05555\times\) & & \\  & & & \(X7-7.113\times X8+3.988\times X9\) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Actual**} \\ \cline{3-4}  & & **Normal** & **Defective** & **Sum** \\ \hline \multirow{4}{*}{Predict} & \multirow{2}{*}{normal} & 281 & 1 & 282 \\  & & (97.9\%) & (9.1\%) & (94.6\%) \\ \cline{3-4}  & & 6 & 10 & 16 \\ \cline{1-1}  & & (2.1\%) & (90.9\%) & (5.4\%) \\ \cline{1-1}  & & sum & 287 & 11 & 298 \\ \cline{1-1}  & & (100\%) & (100\%) & (100\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Model accuracy confusion matrix. strategy to predict whether the subplates should be cold straightened. This method improves the product quality of subplates effectively. Not only does this application solve the decision-making problem of whether subplates should be cold straightened or not, but also it reduces the costs of cold straightening of all products and the risk of product defects caused by cold straightening. Consequently, this application has important meanings in practical engineering application. In order to compare the advantages and disadvantages of the MMLCS algorithm in this paper with other classical algorithms, we set up a comparative experiment. A contrast experiment which is used to predict the yield of steel plates is performed in the view of the steel plate cold straightening auxiliary decision algorithm based on the multi-machine learning competition strategy (MMLCS) and other classical machine learning decision algorithms (SVM, decision tree, and naive Bayes algorithm as examples). The experimental results are shown in Figure 6. In terms of the good product hit rate, naive Bayes gets the highest hit rate (93.10%) among three classical machine learning decision algorithms. In comparison, the MMLCS gets 97.90% in the good product hit rate. In terms of the defective hit rate, naive Bayes gets the best hit rate (88.60%) among the classical machine learning decision algorithms in the experiment, while MMLCS gets 90.90% in the defective hit rate, which achieves a better performance. In conclusion, the MMLCS steel plate cold straightening decision algorithm in this paper is better than the other three classic machine learning decision algorithms in terms of the good product hit rate and the defective hit rate.