# Dynamic Bottleneck Identification of Manufacturing Resources in Complex Manufacturing System
## 4 Results
### Experimental Study  
The test computer's CPU frequency is 1.90 GHz and 2.11 GHz, memory is 16 GB, and the running environment is the Windows 10 operating system. By running the simulation system, the production process data of the CMS are obtained. This article selects a time window with a period of 300 min to analyze the bottleneck of the complex system after one week of simulation warm-up. Table 7 shows the buffer content data. It can be seen from the table that only buffer 4 is not full or empty during the simulation operation. According to the definition of an effective buffer, buffer 4 is effective, decoupling the system into two parts. The first part of the system comprises equipment 1, equipment 2, equipment 3, and equipment 4, and the second part is composed of equipment 5 and 6. Therefore, we first analyze the fine-grained status of the two subsystems and then analyze the overall bottleneck situation of the system through CFBI. After processing the simulation data, the dynamic bottleneck state of the CMS is obtained, as shown in the Figure 12. Take Subsystem 1 as an example to explain the dynamic bottleneck phenomenon in the production process. First, the fine-grained state of the equipment is analyzed and the effective state is extracted. The yellow area in Figure 12a(1) is the effective fine-grained state of the extracted machine. Then, the overall bottleneck status of the subsystem according to the effective status is obtained, as shown in \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Workpiece Category**} & \multicolumn{6}{c}{**Planned Processing Time (s)**} \\ \cline{2-7}  & **M1** & **M2** & **M3** & **M4** & **M5** & **M6** \\ \hline Brake disc & 18 & - & 24 & - & 42 & - \\ Output shaft & - & 18 & - & 18 & - & 84 \\ Traction wheel & 48 & - & 24 & - & - & 98 \\ Coupling & - & 30 & 24 & - & 78 & - \\ Brake arm & 36 & - & 42 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Parts processing information. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Machine** & **Collection Object** & **Technical Parameter** \\ \hline Sensor & & The sampling upper limit frequency is 36 KHz. \\ (PCB356A03) & & Vibration signal & The sampling upper limit frequency is 51.2 KHz. \\ \hline Data acquisition card & Acoustic signal & The sampling upper limit frequency is 51.2 KHz. \\ (NI9234) & Vibration signal & The dynamic range is 102 DB. \\ \hline \multirow{2}{*}{Machining Center} & Processing parameters, & XYZ axis maximum stroke, main motor power, spindle \\  & Spindle load, etc. & speed, positioning accuracy \\ \hline High-frequency reader & & & IP64 level waterproof/dustproof \\ (ALR-F800) & & RFID Label & Working range 865 HZ–960 HZ \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Data collection equipment and main parameters. Figure 12a(2), where the blue area is a single bottleneck stage, and the red zone is a bottleneck stage. Different bottleneck stages divide the time window into several segments. For each segment, the CFBI is used to calculate the comprehensive bottleneck of the equipment. The calculation results are shown in Table 8. The bold parts in the table are the system bottlenecks in the corresponding period. The calculation results of the bottleneck degree are consistent with the results of the dynamic bottleneck identification method shown in Figure 12, proving that the proposed method is effective. In the same period, the comprehensive bottleneck degree of different machines is different, which indicates that different machines have different degrees of restriction on the system. Therefore, system resources should be allocated according to the bottleneck to maximize system benefits. In addition, it can be seen that the bottleneck machines are different in different periods, which indicates that the bottleneck has moved.