## 5 Model Training and Results for Roughness Prediction  
In the beginning of the project, the process engineers had a hypothesis about the quality property and the effects of the process parameters on it. They presumed that the preceding hot rolling process has a strong effect on the RAP surface quality, and process parameter corrections in that step could improve the quality of the final product. Our counter-hypothesis was that there are some specific process settings in RAP line that affect the quality, and the aim of our research was to find out how much the hot rolling actually affects the final product's surface quality. Based on that question, we formed three GBM models for steel strip surface roughness prediction: Model_HYBRID includes both the hot rolling variables and the RAP-process variables, Model_HOT includes only the hot rolling variables, and Model_RAP includes only the RAP-process variables.  
The models were implemented with R, which has been used in several industrial applications because of its flexibility and compatibility. For online use, a tool that combines the process data with the models and presents the visualizations is needed. [PERSON] et al. [10] present a quality monitoring tool (QMT) that enables the online visualization of the production line with information about the predicted quality for each product. The methods presented in this paper can be implemented in the QMT tool.  
In this study, the main data preprocessing tasks were data cleaning, such as eliminating the noise and handling the inconsistent data, data integration from different sources,and data reduction, including the selection and extraction of features. The data were divided into four parts by steel type in chronological order. The first 80% from each steel type group was selected as the training set and the last 20% as the test set. Thus, each steel type is represented in both sets.  
The variables used in models are measured from the hot rolling line and RAP line, and additional information about the chemical composition and the dimensions of the slab was used as well. In total, the original data set consisted of information about 128 process variables, which should be reduced significantly because the number of eligible observations was 206. After careful feature extraction and selection, there were 16 variables used in Model_HYBRID, 16 variables in Model_HOT, and 15 in Model_RAP.  
The performance and generalization based on the test set results of the GBM models are shown in Table 1. The overall correlation between the target and estimated values of the test set was 0.87 for Model_HYBRID, 0.87 for Model_RAP, and 0.81 for Model_HOT, respectively. Scatter plots for predicted and actual values can be seen in Figures 1-3. The observations in Model_HOT are a bit more scattered than in Model_RAP and Model_HYBRID. Figure 1 shows that Model_HOT cannot produce a predicted roughness greater than 3.8. In contrast, Model_RAP and Model_HYBRID perform better, as can be seen in Figures 2 and 3. According to the root mean squared errors (RMSEs), the variables of the RAP line can actually explain the surface roughness better when compared to Model_HOT; the difference between RMSEs is 0.1. The difference between Model_RAP and Model_HYBRID is marginal. The later analysis will reveal some reasons for the weaker performance of Model_HOT.