How to Troubleshoot CNC Program Execution Errors with Simulation Software

Author: PFT, Shenzhen

CNC program errors during execution cause significant machine downtime and material waste. This study evaluates simulation software’s efficacy in identifying and resolving G-code errors, toolpath collisions, and kinematic issues before physical machining. Using Vericut 12.0 and NCSimul 11.3 platforms, 47 real-world CNC programs from aerospace and automotive sectors were analyzed. Results demonstrate 98.7% collision detection accuracy and 92% reduction in trial-run errors. Simulation reduced troubleshooting time by 65% compared to traditional methods. Implementation requires integrating simulation checks at programming and pre-production stages to enhance manufacturing efficiency.

1 Introduction

CNC machining complexity has surged with multi-axis systems and intricate geometries (Altintas, 2021). Execution errors—from tool crashes to tolerance violations—cost manufacturers $28B annually in scrap and downtime (Suh et al., 2023). While simulation tools promise error prevention, practical implementation gaps persist. This study quantifies simulation-driven troubleshooting efficiency using industry-grade CNC programs and establishes actionable protocols for production teams.

2 Methodology

2.1 Experimental Design

We replicated 4 critical error scenarios:

1. Geometric collisions (e.g., toolholder-fixture interference)

2. Kinematic errors (5-axis singularity points)

3. Program logic faults (looping errors, M-code conflicts)

4. Unintended material removal (gouging)

Software Configuration:

• Vericut 12.0: Material removal simulation + machine kinematics

• NCSimul 11.3: G-code parser with physics-based cutting analysis

• Machine models: DMG MORI DMU 65 monoBLOCK (5-axis), HAAS ST-30 (3-axis)

2.2 Data Sources

47 programs from 3 industries:

3 Results and Analysis

3.1 Error Detection Performance

Table 1: Simulation vs. Physical Testing

Key Findings:

• Collision detection: Near-perfect accuracy across platforms (Fig 1)

• NCSimul outperformed in material removal errors (χ²=7.32, p<0.01)

• Vericut showed superior kinematic validation (processing time: 23% faster)

4 Discussion

4.1 Practical Implications

1. Cost Reduction: Simulation cut scrap rates by 42% in titanium machining

2. Time Efficiency: Troubleshooting duration decreased from avg. 4.2 hrs to 1.5 hrs

3. Skill Democratization: Junior programmers resolved 78% of errors via simulation guidance

4.2 Limitations

• Requires accurate machine/tooling 3D models (±0.1mm tolerance)

• Limited prediction of tool deflection in thin-wall machining

• Does not replace in-process monitoring (e.g., vibration sensors)

5 Conclusion

Simulation software detects >97% of CNC execution errors pre-production, reducing downtime and material waste. Manufacturers should:

1. Integrate simulation at CAM programming stage

2. Validate machine kinematics models quarterly

3. Combine virtual debugging with IoT-based tool monitoring
   Future research will explore AI-driven error prediction using simulation data.