Strain Mapping
Chart overview
Strain mapping provides spatially resolved measurements of deformation fields across sample surfaces, revealing heterogeneous strain localization, crack initiation zones, and microstructural stress concentrations that are invisible to bulk extensometry.
Key points
- Techniques include digital image correlation (DIC) on speckle-coated specimens, electron backscatter diffraction (EBSD) kernel average misorientation maps, and finite element simulation output.
- These 2D strain fields are critical for validating constitutive models, identifying failure-prone microstructural features, and understanding deformation mechanisms in metals, composites, and geological materials.
Python Tutorial
How to create a strain mapping in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
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"Create a publication-quality 2D strain map from my DIC or EBSD data. Plot the spatial coordinate grid with x (mm or um) on the x-axis and y (mm or um) on the y-axis. Color each pixel or element by the strain component value (e.g., exx, eyy, von Mises equivalent strain) using a diverging colormap (e.g., RdBu) for signed strain with zero centered, or a sequential colormap for absolute strain magnitude. Add a colorbar with units (or dimensionless), axis labels with units, strain component label in the title, and a scale bar. Overlay grain boundaries or specimen outline if available. White background."
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Python Code Example
Console Output
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Common Use Cases
- 1Identifying strain localization bands and shear band formation in deformed metals
- 2Validating finite element models against full-field DIC measurements during tensile tests
- 3Mapping residual strain around welds, notches, and crack tips in structural components
- 4Correlating grain-scale plastic strain heterogeneity with crystallographic orientation from EBSD
Pro Tips
Use a diverging colormap centered at zero for signed strain components (tension/compression)
Add a scale bar directly on the map image rather than relying solely on axis tick labels
Mask or outline the specimen boundary to distinguish measured area from background
Plot the strain histogram alongside the map to quantify mean and standard deviation of the field
Long-tail keyword opportunities
High-intent chart variations
Library comparison for this chart
matplotlib
Best when you need full control over axis formatting, annotation placement, and journal-specific styling for strain-mapping.
numpy
Useful in specialized workflows that complement core Python plotting libraries for strain-mapping analysis tasks.
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