## 5 Discussion: Industry 5.0 Framework and Complex Systems Simulations
### Industry 5.0 Workflow Design and Process Simulation
By hypothesizing a production line constituted by a single robot that handles a workpiece that will be processed successively by a machine (simplification of the production line layout of Figure 3a), the BPMN workflow of Figure 6 illustrates the interaction between the three processes that integrate the AI facilities (highlighted in red). The main process is represented by the first pool, which describes the robot and machine control flux performed by sensors, and actuation actions performed by the AI by adjusting by according to feedback systems from the robot and machine parameters during the continuous production. Digital production data are transferred to a data analysis engine able to activate predictive maintenance interventions by adopting AI based decisions (AI predicting product defects and machine failure) and processing simultaneously the electrical power data of machines. An anomalous electric load could represent a machine start failure. The data, stored in a big data system, are also processed for quality assessment processes. Also, in this case, the AI algorithms could improve the whole production process by using its quality prediction. The proposed approach is based on the Industry 5.0 framework design and simulation. The framework conceptualization was extracted by analyzing the state of the art concerning the specific application field and technologies associated to an industry in the high-tech manufacturing sector. The limitations of the framework are mainly in the interoperability  
Figure 5: Heat map: diffusion heat simulation of the complex Industry 5.0 model enhancing central elements of the industry upgrade (_Big Data, Actuator Engine L1, AI Edge Computing, Quantum Computer_). between all sub-processes. Table 7 lists some important limitations and the associated perspectives as possible solutions. \ \begin{table}
\\begin{tabular}{p{113.8 pt} p{113.8 pt}} \\hline \\hline
**Limitations** & **Perspectives** \\\\ \\hline Use of a unique informatics infrastructure integrating all data protocols (different technologies use different protocols and infrastructures). & Design of ESB-based information networks integrating all data protocols and managing synchronization. \\\\ \\hline Synchronization of all robots and machines (the different sampling times of sensors could generate problems around the efficiency of synchronization of all robots and machines of a production line). & Development of codes managing the sampling time and time machines according to sensor technologies and production velocity. \\\\ \\hline Computational cost for real-time data processing (a large number of sensors provide a big quantity of data to be processed at the same time, thus incurring a high computational cost). & Cloud computing could be inappropriate for the real time data processing and big data analytics, but an edge computing approach could be a good alternative. \\\\ \\hline The gap between the velocities of production machines and high-velocity optical system detecting data. & Production machines that integrate all opto-electronic systems (fully optical sensing and actuation systems). \\\\ \\hline Efficiency of quantum computing (standalone qubit processor could be not appropriate for the data processing of industrial datasets). & Combined use of AI algorithms executed by edge computing methods and a quantum computer optimizing AI hyperparameters. \\\\ \\hline \\hline \\end{tabular}
\\end{table} 
Table 7: Framework limitations and perspectives to overcome these limitations. Figure 6: BPMN model “exploding” Industry 5.0 processes and correlations between robot/machine control, predictive maintenance, and quality assessment.