## 5 Results and Discussion  
### Process Monitoring and Control  
For the control case, an expert would be required to intervene, leading to human-in-the-loop optimization and empirical control, since, as shown below, the automated optimization is possible, at least with the available techniques (cost is also a restriction). To this end, in the following figures, the efficiency of various controllers with respect to tracking at 650 K is shown. To begin with, Figure 8 is related to a proportional-integral-derivative-based (PID) controller enforced on a simple dynamic system. The problem here lies with the fact that there is a cooling down phase, during which the dynamic system modelling is no longer valid. Thus, a switched system has to be developed, allowing the dynamics of the system to change.  
Using a bang-bang controller [116] and a dynamic system, however, excludes the integration of automated controller calibration. Figure 9 illustrates the effect of a sophisticatedly selected bang-bang controller. It has a rather undesired performance due to it overshooting.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Scenario 1** & **Scenario 2** & \(\ldots\) \\ \hline Condition 1 (Large Diameters) & & \(\ldots\) & \(\ldots\) \\ Condition 2 (Small Diameters) & True & \(\ldots\) & \(\ldots\) \\ Condition 3 (Large Tolerance) & & \(\ldots\) & \(\ldots\) \\ Condition 4 (Small Tolerance) & True & \(\ldots\) & \(\ldots\) \\ Condition 5 (OPC-UA) & True & \(\ldots\) & \(\ldots\) \\ Condition 6 (MQTT [114]) & & \(\ldots\) & \(\ldots\) \\ \(\ldots\) & & \(\ldots\) & \(\ldots\) \\ Condition N & & \(\ldots\) & \(\ldots\) \\ \hline Action 1 (Choose Lathe 1) & X & & \\ Action 2 (Choose Lathe 2) & & & \\ \(\ldots\) & & & \\ \hline \end{tabular}
\end{table}
Table 2: Augmented decision table for technology-enhanced computer-aided process planning (CAPP).  
For this reason, a bang-bang controller with a lower frequency, as well as different extreme values for the power were developed; the performance of the closed-loop system is satisfactory, as per the graph of Figure 10.  
Figure 8: PID controller applied in a thermal manufacturing process: tracking efficiency.  
Figure 9: Gain controller applied in a thermal manufacturing process modelled as a switched system: tracking efficiency.  
It is evident that the original workflow of Figure 6 is not adequate to address the optimization problem. For this reason, the classification can be highly useful. In Figure 11, a new workflow is attempted, integrating the classification paradigm; the dashed boxes are generated in the procedure, due to alarms generation, while the whole workflow has been linearized due to constraints being imposed at the beginning (one can strengthen this characteristic adding human-in-the-loop optimization and thus enforce empirical constraints).  
The role of the expert would be centered in metamodelling and control selection, primarily. This will help achieve extra goals such as human inclusion. Tools and technologies, such as artificial intelligence (AI) and high-performance computing (HPC) help in achieving this to a larger extent.  
Figure 11: Updated workflow for process monitoring/control taking into account metamodelling.  
Figure 10: PID controller applied in a thermal manufacturing process modelled as a switched system: tracking efficiency. The controller was empirically calibrated.