## 3 Results
### Primary and Secondary Coating Area Predictions
\begin{table}
\begin{tabular}{l c c c c} \hline
**Classifier** & **Accuracy** & **MAE** & **RMSE** & **AUC** \\ \hline By vote (minimum probability) & 59.30 & 0.30 & 0.54 & 0.51 \\ By vote (maximum probability) & 68.75 & 0.42 & 0.46 & 0.57 \\ By vote (average of probabilities) & 65.14 & 0.41 & 0.47 & 0.57 \\ By vote (product of probabilities) & 59.30 & 0.30 & 0.54 & 0.51 \\ By vote (majority voting) & 68.41 & 0.32 & 0.55 & 0.60 \\ \hline Grading (meta: naive Bayes) & 67.46 & 0.33 & 0.56 & 0.57 \\ Grading (Bayes network–TAN) & 65.95 & 0.34 & 0.58 & 0.55 \\ Grading (meta: K-nearest neighbors with K = 1) & 67.73 & 0.32 & 0.56 & 0.58 \\ Grading (meta: K-nearest neighbors with K = 2) & 67.73 & 0.32 & 0.56 & 0.58 \\ Grading (meta: K-nearest neighbors with K = 3) & 68.02 & 0.32 & 0.56 & 0.58 \\ Grading (meta: K-nearest neighbors with K = 4) & 68.02 & 0.32 & 0.56 & 0.58 \\ Grading (meta: K-nearest neighbors with K = 5) & 68.02 & 0.32 & 0.56 & 0.58 \\ Grading (C4.5) & 65.82 & 0.34 & 0.58 & 0.54 \\ \hline Stacking (meta: naive Bayes) & 63.34 & 0.37 & 0.57 & 0.53 \\ Stacking (Bayes network–TAN) & 68.59 & 0.43 & 0.46 & 0.50 \\ Stacking (meta: K-nearest neighbors with K = 1) & 66.29 & 0.37 & 0.53 & 0.58 \\ Stacking (meta: K-nearest neighbors with K = 2) & 67.54 & 0.38 & 0.51 & 0.55 \\ Stacking (meta: K-nearest neighbors with K = 3) & 66.62 & 0.39 & 0.51 & 0.54 \\ Stacking (meta: K-nearest neighbors with K = 4) & 67.05 & 0.39 & 0.50 & 0.53 \\ Stacking (meta: K-nearest neighbors with K = 5) & 68.12 & 0.39 & 0.50 & 0.55 \\ Stacking (C4.5) & 63.25 & 0.42 & 0.51 & 0.54 \\ \hline Multi-scheme (Bayes network–TAN) & 67.66 & 0.42 & 0.47 & 0.58 \\ \hline \end{tabular}
\end{table} 
Table 12: Results predicting the porosity apparition when we applied a combination of classical machine learning methods through a meta-classifier using data extracted from both primary and secondary coating areas in addition to melting data. The best meta-classifier, marked in gray, was the by vote under maximum probability method, with an accuracy level (percentage of correctly classified instances) of close to 69%. \begin{table}
\begin{tabular}{l c c c c} \hline
**Classifier** & **Accuracy** & **MAE** & **RMSE** & **AUC** \\ \hline Stacking (meta: naïve Bayes) & 73.67 & 0.26 & 0.47 & 0.83 \\ Stacking (Bayes network–TAN) & **76.83** & **0.26** & **0.42** & **0.80** \\ Stacking (meta: K-nearest neighbors with K = 1) & 70.21 & 0.30 & 0.50 & 0.71 \\ Stacking (meta: K-nearest neighbors with K = 2) & 71.26 & 0.31 & 0.47 & 0.75 \\ Stacking (meta: K-nearest neighbors with K = 3) & 74.24 & 0.31 & 0.45 & 0.76 \\ Stacking (meta: K-nearest neighbors with K = 4) & 76.43 & 0.32 & 0.44 & 0.77 \\ Stacking (meta: K-nearest neighbors with K = 5) & 75.81 & 0.32 & 0.43 & 0.78 \\ Stacking (C4.5) & 70.52 & 0.33 & 0.48 & 0.70 \\ \hline Multi-scheme (Bayes network–TAN) & 71.10 & 0.30 & 0.46 & 0.78 \\ \hline \end{tabular}
\end{table} 
Table 11: _Cont._Finally, it should be noted that the models used in this study do not require high computational costs. To enhance the readability of this paper, a brief summary is provided below instead of presenting all the detailed tables. For classical classifiers, training times do not exceed 0.09 s per fold (in the case of ANN with MLP), while testing times do not even reach 0.0003 s. In the case of meta-classifiers, learning times are higher, reaching about 3 s per fold in the worst cases (i.e., using grading). However, testing times do not exceed 0.01 s. These results indicate that these predictors could be used in a real-time solution.