# Cloud-Edge Collaboration-Based Knowledge Sharing Mechanism for Manufacturing Resources
## 5 Experiment
### Prototype Platform and Manufacturing Tasks  
A prototype platform of a multi-variety, mixed-flow intelligent manufacturing production line is shown in Figure 6. The main system workflow is as follows. First, the user utilizes a web page or a mobile app to select the product's type, quantity, and personalized pattern. The user order is completed in the cloud. Once recorded, the order is issued directly to the manufacturing resource edge, generating the control flow related to the processing task. The control flow drives each manufacturing resource to perform the current processing task. The equipment utilization is optimized through a high degree of coordination and knowledge obtained in previous processing operations. The manufacturing production line's efficiency is reflected in the multi-task collaborative production and dynamic production line reconfiguration. The mixed-flow manufacturing functions incorporated in the prototype platform to date include, for example, custom wood carving crafts processing, personalized U disk customization, and Bluetooth remote control assembly. In the prototype platform, Robot1 and CNC1 are matched, as well as Robot2 and CNC2, and are responsible for the clamping and engraving process of wood carving originals. The laser machine performs laser printing according to the pattern and text selected by the user when placing the order. Loading machine loads the product packaging/box. Robot3 and Robot4 cover the processed product packaging, whereas Robot5 uses machine vision to select the required raw materials for loading operations. The final products in the packaging box are uniformly unloaded. The specific functions of manufacturing resources are shown in Table 2. The prototype platform involved in this experiment is a multi-variety mixed-flow platform, so the order of each station in the processing process cannot only satisfy a single product manufacturing process. To solve this problem, we have adopted a method of station matching. When the product passes through the station, the product manufacturing information will be read. If the product needs to be processed by this station, it will enter the corresponding production branch line, and if it is not needed, it will move directly to the next station. \begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Definition** \\ \hline \(\overset{\rightarrow}{v}\) & Feature vector \\ \(i\) & Action \\ \(r\) & Resource \\ \(s\) & Skill \\ \(a\) & Action Description \\ \(e\) & Environment \\ \(r_{h}\) & Digital signature \\ \(w_{n}\) & Weight \\ \(Q_{n}\) & Serial signature \\ \(S_{n}\) & Standardized signature \\ \(C_{l}\) & Action collection \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Definition of Symbols. The experiment starts by the loading machine that reads the products' order information. The loading machine writes the packaging boxes' RFID tags, including the involved process and the corresponding processing actions. The pallets carrying the packaging boxes and materials pass through various stations. The RFID tags are read at the station, and the processing actions that can be performed at the current station are executed. The relationship between the physical manufacturing resources in the prototype platform and the action primitives that can be executed is shown in Table 3. It can be seen from the table that each physical resource processing can be regarded as a collection of actions, and the action primitives it contains have a similar composition structure.