Python for Engineering Automation
How to use Python to automate engineering workflows — FEA result extraction, batch processing, parametric studies and report generation.
Engineering Task
Using Python to automate repetitive engineering workflows — extracting FEA results, running parametric studies, processing test data and generating reports.
Core Libraries
| Library | Engineering Use |
|---|---|
| NumPy | Array operations, matrix algebra, linear algebra |
| SciPy | Integration, optimisation, signal processing, interpolation |
| pandas | Load spectrum management, tabular FEA results, data I/O |
| Matplotlib | S-N curves, load-displacement plots, stress charts |
| meshio | Read/write FEA mesh formats (Abaqus, Nastran, VTK) |
| pyvista | 3D visualisation of FEA results without a commercial post-processor |
Reading FEA Results
# Read Abaqus .rpt file with pandas
import pandas as pd
df = pd.read_csv("results.rpt", skiprows=15, delim_whitespace=True,
names=["Element", "S11", "S22", "S12", "Mises"])
max_mises = df["Mises"].max()
print(f"Peak von Mises: {max_mises:.1f} MPa")Batch Processing
- Use glob to iterate over multiple result files
- Extract key results (peak stress, max displacement, reactions) from each
- Store results in a pandas DataFrame for comparison and enveloping
- Generate summary plots and tables automatically
Best Practices
- Use SI units consistently throughout; document unit assumptions at the top of each script
- Write functions rather than scripts for reusable calculations — easier to test and maintain
- Add type hints to function signatures for engineering calculations
- Version control all scripts — treat analysis code like analysis reports
- Write unit tests for hand calculation implementations using pytest