1 Equipment and Methods

1.1 Data Sources and Measurement Framework

Operational data was collected from factory shift records (January–September 2025), machine-tool diagnostic outputs, and automated inspection logs. To ensure repeatability, the evaluation adopted fixed measurement windows: 60-minute utilization sampling, full-cycle machining timing, and gauge-controlled dimensional checks. Environmental parameters—temperature, coolant concentration, spindle load—were recorded to maintain consistent conditions across measurements.

1.2 Equipment Inventory and Classification

1.2.1 CNC Milling Systems

The facility operates 3-axis and 5-axis vertical machining centers equipped with high-speed spindles ranging from 12,000 to 20,000 rpm. Each unit includes integrated probing modules that support in-process measurement. Tool magazines contain 20–60 positions, enabling rapid transitions between complex features.

1.2.2 CNC Turning Platforms

Turning systems include dual-spindle lathes and power-turret configurations designed for simultaneous machining. Bar feeders support continuous processing of stainless steel, aluminum, and titanium stock up to 65 mm in diameter.

1.2.3 Auxiliary and Inspection Equipment

Auxiliary systems include automatic pallet changers, robotic loading arms, and coolant recycling units. Dimensional verification relies on CMMs, high-resolution optical comparators, and portable articulated measurement arms.

1.3 Workflow Modeling and Reproducibility

1.3.1 Process Flow Mapping

Process steps—program loading, fixture setup, rough machining, semi-finishing, finishing, deburring, and inspection—were mapped using a standardized workflow chart. Each stage was time-stamped and logged through a digital MES interface to ensure reproducibility.

1.3.2 Capacity Simulation Model

A discrete-time simulation modeled spindle uptime, setup duration, and inspection intervals. Inputs included actual tool-life records and verified machine cycle times. The model is designed for replication by applying identical time parameters and machine states.

2 Results and Analysis

2.1 Throughput Performance

2.1.1 Machining Cycle Time

Data indicates that integrating 5-axis machining reduces repositioning frequency, generating an average cycle-time improvement of 18–23% compared with earlier 3-axis-only workflows. Automated probing decreases offset-adjustment periods by approximately 12 seconds per check.

2.1.2 Equipment Utilization

Measured spindle utilization across three shifts reaches 78–84%, exceeding common industry benchmarks by 6–8 percentage points. Robotic loading units stabilize utilization during small-batch runs, where manual loading typically introduces variability.

2.2 Dimensional Accuracy and Consistency

Average dimensional deviation remains within ±0.008 mm across 500 recorded components. Optical inspection data confirms that consistent tool-path optimization reduces surface-finish scatter, particularly on aluminum housings and precision shafts.

2.3 Benchmark Comparison

Published machining studies from 2019–2023 report average small-batch utilization rates between 65–76%. The observed 2025 performance reflects the influence of synchronized scheduling and multi-axis integration, aligning with recent findings on digitalized factory operations.

3 Discussion

3.1 Factors Influencing Cycle-Time Reduction

Reduced cycle times result primarily from consolidated tool paths, fewer manual adjustments, and faster in-process inspection. Enhanced spindle acceleration profiles also contribute to overall efficiency gains.

3.2 Limitations

Capacity results are influenced by the factory’s specific product mix, which predominantly involves medium-complexity aluminum and stainless-steel parts. Results may vary for heavy-cutting scenarios or materials requiring extended coolant stabilization.

3.3 Practical Implications

Consistent utilization and stable dimensional performance suggest that multi-axis systems combined with robotic handling can support both high-precision and high-mix production. Workflow data can guide future decisions on fixture standardization and automated inspection integration.

4 Conclusion

The 2025 operational assessment shows that coordinated equipment upgrades and digital workflow mapping significantly improve machining consistency and factory-level productivity. Cycle-time reductions, enhanced utilization, and stable dimensional outcomes demonstrate the value of integrated multi-axis systems. Future work may explore additional automation in deburring and final inspection to expand throughput during peak production periods.