## 3 Application of the Process Method
### Enhanced Expert System Design  
The unique method described in this paper determines a smart hydraulic press with the integrated real-time PR-MPC mechanism within an expert system that considers the external disturbances, such as the forming process and the dynamic friction characteristics, caused by the hydraulic system sealing and guiding system, as well as the hydraulic press guides, as shown in Figure 6. The response of the hydraulic system is affected by the pronounced impact of the sheet metal bending, due to specimen deformation acting upon the movement of the hydraulic cylinder. In addition, the dynamic frictional characteristics also affect the response of the hydraulic system response, albeit to a lesser extent. To obtain the most relevant information, the experimental data are used as training data to enhance the best performance for data-driven models. The data collection phase was conducted with experiments to collect historical data to study various what-if scenarios and gather a priori knowledge about the behavioral aspects of the hydraulic press. In addition, polynomial regression models were developed and employed as algorithms in the Beckhoff CP3718 controller for each phase of the sheet metal bending cycle, i.e., bending, leveling, and movement. The primary objective of the smart algorithm is to predict explanatory variables, as demonstrated in our case study, by estimating the displacement error between the measured and reference hydraulic cylinder positions. This is achieved through a data-driven approach utilizing the existing measured data. Furthermore, the aim of the PR-MPC mechanism within an expert system is to enable real-time prediction and process monitoring. The effectiveness of the control system was evaluated by comparing the results of the initial control strategy and the improved control strategy, whereby the stability of the hydraulic press was determined by the magnitude of the hydraulic press response error \(\Delta X_{\text{C}}\). To integrate modularity into the hydraulic press, the hydraulic press network was upgraded to a 5G network [46]. The 5G technology was selected based on its high-speed, low-latency specifications, enabling remote control capabilities and ensuring enhanced cybersecurity [47; 48; 49]. The 5G network operates in non-stand-alone mode (NSA), which means that it uses the long-term evolution (LTE) network for connecting the devices and the LTE and 5G network for data transmission. Moreover, both 4G and 5G networks are available in parallel to achieve rapid data transfer between control PC and hydraulic press with PLC. The following base station equipment is installed in the laboratory environment to enable 5G communication between our remote-control system (PC) and the Beckhoff PLC integrated into the hydraulic press (set up on channel N78, i.e., in the 3.5 GHz band):  
* ERRICSSON radio system RAN 6507 (Ericsson, Stockholm, Sweden),
* ERRICSSON 5G-network cell (Ericsson, Stockholm, Sweden),
* ERRICSSON LTE/4G-network cell (Ericsson, Stockholm, Sweden). The proposed expert system for hydraulic presses includes modules of human intelligence that offers solutions and draws conclusions in a nonlinear way, uses common sense and intuition to solve problems, reveals a human emotional intelligence, and mostly offers the quality of its knowledge base. For instance, the expert system runs an ML model to gather information and automatically detect the changed state of the hydraulic press. Secondly, the expert system uses time-dependent variables that are updated in real time using an ML model. An ML model interrogates an expert system to recognize and suggest whether the decision rules are eligible. A valid ML model is necessary to interpret the expert's findings and knowledge engineering. Awareness of the hydraulic press's behavioral characteristics is critical for identifying behavioral flaws and providing solutions solely by interpreting the experimental data and the virtual model data that reflect the physical system's characteristics.