## 5 Conclusions and Future Work  
In this paper, a real-time adaptive control strategy using polynomial regression modeling was introduced to improve the stability of a hydraulic press affected by external disturbances, such as the forming process and frictional dynamics. The PR-MPC mechanism integrated into an expert system monitors the condition of the hydraulic press and provides solutions with decision-making algorithms. The simplicity of the ML models, with fewer complementary variables, contributed to a less complex RTACA algorithm. The conclusive results show that the proposed method is sufficiently accurate as follows:  
1. The hydraulic press working area is sufficiently determined by the observational area and the validation area, considering the operating limits of the hydraulic press and the external disturbances investigated in the study. In the data analytics phase, valuable insights into the anticipated behavior of the hydraulic press and the effectiveness of adaptive control in the physical environment are derived from historical data. 2. As a modeling approach, polynomial regression models can accurately determine the response error of the hydraulic press by learning the relationships between pre-determined predictors. The RSM method effectively visualizes the relationship between dependent and independent variables within the dataset, assisting in the determination of the appropriate order and dimensions for the polynomial fit. The introduction of the least squares estimator into a hyperparameter optimization and determination resulted in the accurate performance of the ML models. The \(R^{2}\) valuesfor the sheet metal bending cycle in the case of the experimental analysis are as follows: 0.99 bending phase, 0.95 leveling phase, 0.99 movement phase. 3. A PR-MPC mechanism integrated into the expert system proves to be an accurate solution for a hydraulic press subjected to the forming process and friction dynamics. The efficiency of the adaptive control benefits from the decision-making and ML models as foreseen in the actual experimental analysis. The improvement with the PR-MPC mechanism integrated into the expert system shows an improvement of 89.6% to 98.6% in the absolute response error of the hydraulic press compared to the initial control system. 4. The integration of a 5G NSA communication network between the local cloud, hosted on a local computer, and the PLC controller of the hydraulic press enables efficient remote control of the smart forming process. Furthermore, the hydraulic press benefits from the ability to operate remotely, supervise behaviour in real time, and monitor the production process, leveraging the high speed and low latency of future 5G networks. Conceptualization, D.J. and M.P. ; methodology, D.J. and M.P. ; software, D.J. ; validation, D.J., M.P., M.S. and N.H.; formal analysis, D.J. and M.P. ; investigation, D.J. and M.P. ; resources, N.H.; data curation, D.J. ; writing--original draft preparation, D.J. and M.P. ; writing--review and editing, M.S. and N.H.; visualization, D.J., M.P., M.S. and N.H.; supervision, M.S. and N.H.; project administration, N.H.; funding acquisition, N.H. All authors have read and agreed to the published version of the manuscript. This research was funded by the Slovenian Research and Innovation Agency--ARIS, research project J2-4470 and research program P2-0248. This research was funded by the European Union under the Horizon Europe Grant N\({}^{\circ}\)101087348, project INNO2 MARE and Grant N\({}^{\circ}\)101058693, project STAGE. Not applicable. The original contributions presented in this study are included in the article; further inquiries can be directed to the corresponding author. We would like to express our sincere appreciation to Telekom Slovenije, d.d. for their invaluable support in enabling and installing the 5G NSA network in our laboratory. This collaboration provided the essential infrastructure required for conducting advanced 5G testing and research, significantly contributing to the successful execution of our study.