Classification

Use the K-means Clustering tab to cluster and map hardness/modulus data.

Inputs

multiple result files
clustering settings appropriate for your dataset

Typical steps

Load multiple result files.
Choose clustering settings.
Run classification.
Plot the mapping before and after clustering.

Outputs to inspect

Useful checks include:

whether the clusters are stable
whether the mapping matches the expected microstructural regions
whether the input hardness/modulus data were prepared consistently

Key plots

The key plots are:

the mapping before clustering
the mapping after clustering
any cluster-colored property plot that shows whether the clusters are physically meaningful

Common problems

Typical issues include:

mixing inconsistent input datasets
choosing clustering settings without checking the mapping
interpreting clusters without comparing them to the original data

Next step

Classification commonly produces a final analysis view, so the next step is usually export, reporting, or comparison with microscopy or phase information.