## 6 XAI Empowered Decision Support
### Variable Importance  
In GBM, the relative importance of the input variables is determined by their occurrence on the splits during the tree building process and how much each variable then improves the MSE (mean squared error) of the whole prediction model. The importance of the variables of each model for roughness prediction are presented in Figures 4-6. In Model_HOT (Figure 4), the variables that come up are specifically related to tandem rolling (TA) and roughing mill (RM), and the most important are the reduction of the strip at first tandem pass (HOT REDUCTION TA), the specific force of the second (middle) tandem pass (HOT SPECIFIC FORCE TA2), and the rolling force in the middle part of the strip during the first tandem pass (HOT MID ROLL FORCE TA). From the chemical composition of the steel strip, only niobium (NB) has a significant influence. The rolling variables relate the pressure of the roll on the surface and NB contributes to the strength of the strip, and thus they may have a role in roughness forming, but indirectly, from the  
Figure 3: Scatter plot for predicted and actual values in test set for Model_HYBRID.  
Figure 2: Scatter plot for predicted and actual values in test set for Model_RAP.  
effect of mechanical properties. The most important variables in Model_RAP (Figure 5) are all related to the process speed, especially speeds related to pickling and shot blasting, and also slightly to annealing. This is natural, because the pickling speed tells us directly how long the acid attacks the surface and increases the roughness. The annealing speed in turn affects the mechanical properties and the capability of the products surface to resist the effects of the shot blasting. Furthermore, in Model_HYBRID (Figure 6), the RAP process and pickling speeds have a strong influence on roughness. There are also two hot rolling parameters in the top four of the most important parameters. These parameters are important in Model_HOT as well: the reduction of the strip at first tandem pass (HOT REDUCTION TA)) and the diameter of the roll at the fifth roughing mill pass (HOT ROLL DIAM RM5).  
Figure 4: Variable importance for Model_HOT.  
Figure 5: Variable importance for Model_RAP.  
The hot rolling parameters relate strongly to mechanical properties. They may have an effect on surface roughness, but more likely, they are selected because of the strength of the steel. Roll diameter is an exception; it can relate to the area of surface that is in contact or the early phase with new rolls. Either one can be adjusted during the process, but production planning can use the information and schedule the products with the highest quality requirements to a campaign with lower risk. The answer to our first hypothesis is that we cannot improve the surface quality during the process by adjusting only the hot rolling parameters, and in their turn, the RAP variables can explain most of the problem. Thus, only Model_RAP is suitable for assisting the real-time adjustments by RAP-line operators. By combining the parameters from both hot rolling and RAP processes, we can create a hybrid model that gives a wider perspective on the processes. This model is useful for product and production planning and process engineers that want to learn the complete set of relationships. In following analyses, we present the results with Model_HYBRID only.