# Data-Driven Optimization of CNC Manufacturing Using Simulation and DOE Techniques
## 8 Building the Model  
Figure 4 illustrates the Arena simulation model (part entity) used to represent the CNC production system. In the model, the parts arrived at fixed intervals of 30 min, reflecting a constant inter-arrival time. The production line consisted of five main processes: CNC turning, CNC milling, drilling, inspection, and rework loops for defective items. A transfer time of 2 min was incorporated between each station to simulate material handling and movement. The simulation was executed over a total duration of 80 operational hours (equivalent to 16 h per day across five working days), aligning with a typical full-shift schedule. To \begin{table}
\begin{tabular}{c c c c} \hline
**Process Step** & **Part 1 (min)** & **Part 2 (min)** & **Part 3 (min)** \\ \hline Lathe & UNIF (14.5, 20.5) & 9.5 + 6 * BETA (0.551, 0.394) & POIS (23.2) \\ \hline CNC turning & UNIF (24.5, 30.5) & 19.5 + 6 * BETA (0.394, 0.551) & 17.5 + 3 * BETA (0.477, 0.365) \\ \hline Milling & N/A & 14.5 + 6 * BETA (1.17, 0.745) & 11.5 + 4 * BETA (0.886, 0.718) \\ \hline Drilling & TRIA (3.5, 4.8, 5.5) & TRIA (2.5, 3.8, 4.5) & NORM (3.08, 0.337) \\ \hline Inspection & POIS (32.2) & POIS (23.2) & 19.5 + 3 * BETA (0.365, 0.477) \\ \hline Rework & TRIA (2.5, 3.2, 4.5) & TRIA (1.5, 2.2, 3.5) & NORM (3, 0.632) \\ \hline \end{tabular}
\end{table} 
Table 4: Processing data for all parts using the input analyzer. Figure 4: Arena simulation model. ensure robustness and account for variability in system behavior, the model was run over 10 independent replications. This simulation framework enabled a comprehensive analysis of key performance indicators such as cycle time, work-in-progress (WIP), machine utilization, and bottleneck identification, providing critical insights for evaluating and improving the effectiveness of the production system. Figure 5 summarizes the output, utilization, and queue performance of the simulation. The system's overall output of 167 units over the simulation time demonstrated its processing power. Resource 2 and Resource 5 had the highest utilizations (86.34% and 85.27%), suggesting potential bottlenecks. Resource usage varied throughout the stations. Resource 4's low utilization rate of 12.85%, on the other hand, suggested either underuse or excess capacity. These variations are graphically highlighted in the utilization bar chart. Queues 2 and 5 had a moderate buildup in queue performance, which was consistent with high-utilization resources. While some stations ran successfully, others encountered delays that would have affected throughput, according to the queue chart. All things considered, Figure 5 identifies important areas for development, such as balancing resource loads and cutting down on waiting times to increase system effectiveness. Table 5 shows the output results of the simulation, providing important new information on the CNC manufacturing process. The inter-arrival time had a mean of 30 and was distributed exponentially. At 86.54% and 85.27%, respectively, Stations 2 and 5 had the greatest usage rates, suggesting possible bottlenecks as shown in Table 6. Queues 2 (1.778) and 5 (0.6878) had correspondingly longer lines and longer waiting times, indicating delays at these locations. However, at 12.85%, Station 4 was noticeably underutilized, suggesting potential inefficiencies. Changing production loads are reflected in the WIP chart, which displays system flow variability. Confidence intervals for runs of \(n=10\) and \(n=25\) confirmed the consistency of the results. Enhancing system performance, decreasing delays, and increasing productivity can be achieved by addressing bottlenecks at Stations 2 and 5, whether through capacity expansion, task reallocation, or rework reduction. Figure 5: _Cont._