# Cloud-Edge Collaboration-Based Knowledge Sharing Mechanism for Manufacturing Resources
## 5 Experiment
### Experimental Results and Discussion  
The presented multi-variety, mixed production line serves as an experimental platform. The equipment is manually configured and debugged to generate the equipment's initial processing experience (i.e., initial input). Once the ontology modeling on the manufacturing resources' edge is completed, the equipment that has not been manually debugged is configured by relying on the existing knowledge. More precisely, the existing process data model and control instructions are automatically configured. The proposed knowledge sharing mechanism's effectiveness is verified by the experimental results that compare the manually configured action cycle with the automatically configured result. Since Robot1 and Robot2 have similar processing procedures, the two manipulators' action cycles can be read separately for comparison, as shown in Figure 7. Robot1 is manually configured and debugged, and Robot2 is automatically configured based on the knowledge sharing mechanism. The manipulators' main processing procedure can be detailed and decomposed into: conveyor belt material clamping, moving the material to the processing equipment CNC, placing the material, exiting the CNC, waiting for the CNC to enter after the processing is completed, clamping the processed material, and moving the material. The raw materials arrive at the conveyor belt pallet. Figure 7a demonstrates that there is only a slight difference between the two manipulators' action cycles, indicating that Robot2 successfully learned Robot1's existing processing knowledge and experience. Nevertheless, certain differences in the configuration results can be found. In particular, Robot2 chose a more efficient execution plan through comparing the two actions' execution effect evaluations during the learning process. In order to reflect the reduction degree of knowledge reuse, we define the reduction degree \(S_{i}=\frac{|T_{i}-T_{i}|}{T_{i}}\) of action \(i\), where \(T_{i}\) represents the action execution cycle of the knowledge source, and \(T_{i}\) represents the action execution cycle of the device after learning the existing knowledge. The reduction degree of the whole process adopts a weighted average \(\sum\ S_{i}\cdot\frac{T_{i}}{T_{i}}\), where \(T_{a}\) represents the total processing time of the whole process. Combining the calculation of the specific execution cycle data of each action, it can be concluded that the reduction degree of Robot2 for the same task of Robot1 is 95.8%. The action cycles of Robot1, loading machine, and storage machine are selected for comparison. The specific data is shown in Figure 7b. Robot1 and loading machine were manually configured and debugged, while the storage machine's process was learned through the local knowledge base. The figure shows data reading and comparison of the three devices' action primitives. The experimental results demonstrate that the storage machine's action cycle is very similar to that of the loading machine but significantly differs from Robot1's. The storage machine and the loading machine deal with the product's outer  
Figure 7: (a) Action cycles’ comparison for similar manufacturing processes. (b) Multi-process action cycle comparison. packaging. Thus, these machines' operation accuracy is lower than the value of Robot1 when gripping the engraving materials. Consequently, Robot1 has a lower processing speed. Regarding the equipment movement, the storage machine and the loading machine are responsible for the round-trip conveyor belt and the raw material warehousing or the finished product warehousing. These machines do not involve cooperation with other equipment. Robot1 needs to place the engraving materials into the CNC processing area accurately. The CNC's airtightness further restricts Robot1's movement. The three equipment are similar regarding the placement actions: the only difference being the processed object's size, the action cycles do not significantly differ.