## 4 Results and Discussion  
As part of the MUL 4.0 project, four machines were integrated into a value chain [107]. These are positioned at two different localizations. The continuous caster is located at the NFM and is equipped as standard with sensors and DAQ by the manufacturer. The MF houses the furnace, hydraulic press, and rolling mill, posing as the second production site in the process. The rolling mill from 1954 was transformed into a CPPS utilizing low-cost retrofitting and suitable sensors such as Linear Variable Differential Transformer (LVDT) and load cells to be able to integrate required data into the process [52]. In cooperation with the Chair of Industrial Logistics (IL), a cross-process database was set up to make data available between the cooperating parties. Table 3 shows the technical specifications of the sensors associated with the corresponding machines and their location. As visualized in Figure 4, the process chain consists of continuous casting of the aluminum specimens, followed by a variable operation of forming processes. At the MF, the specimen can be cold-formed or rolled. In the hot forming process, the specimen is preheated in the furnace before rolling or upsetting. Subsequently, the samples are subjected to quality control and recycled in the final stage. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type of Data** & **Volume** & **Velocity** & **Value** & **Vercity** & **Variety** \\ \hline Sensor/PLC (e.g., time series data, videos) & low & high & high & high & high \\ \hline Process-related modeling data (e.g., FEA) & high & low & high & medium-high & high \\ \hline Inter \& intra logistical related data (e.g., smart factory DT) & high & high & high & high & (unstructured) \\ \hline WBML & high & Low & high & medium-high & high \\ \hline GBML & medium & medium & high & medium-high & & (unstructured) \\ \hline BBML & low & high & high & high & (unstructured) \\ \hline IIoT & low & high & high & high & high \\ \hline MES & medium & medium & high & high & (structured/semi-structured) \\ \hline ERP & high & low & high & high & high \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of the most important data sources and corresponding qualitative estimation for a digitalized metal processing value chain. To transfer the data recorded by the sensors into the production network, DAQs were implemented (Table 4). At the MF, a low-cost approach was pursued with DAQ systems from Wago GmbH (Brunn am Gebrige, Austria) and the Wago elCockpit software. In addition, data processing, creation and execution of GUIs used with the open-source programming language Python [52]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  
To pursue the low-cost and open-source approach, MariaDB was chosen as SQL-based database, as deemed suitable for the amount of data generated. In the processing chain of MUL 4.0, a distinction can be made between process-related time series data and logistical data. Process-related time series data, e.g., sensor data, data obtained from a FEA or finite volume analysis (FVA), photos and videos need to be accessed and computed in near real-time to create appropriate process DTs. Not all raw data is stored in the database, but selected, filtered data to keep the performance of the database optimal. Data stored in the database can be accessed by authorized users for further data processing or investigation. Furthermore, the MariaDB is linked to an MES and ERP to enable dynamic process chain monitoring, planning and control. As mentioned in Section 2, cyber security plays a key role in the context of I 4.0. For this reason, an IT-layer architecture was designed and implemented as shown qualitatively in Figure 5. The IIoT in layer 2 can be considered as a closed system, from which data from a NodeRed server and the Maria DB are transferred to layer 3.