FIT THE HARDENING CURVES AND RETURN THE CURVE¶
Fits a strain-hardening curve (effective strain vs. effective stress) to a user-selected analytical model (Swift, Voce, Hockett, Stoughton, or MHS) using a neural-network optimiser. Returns the fitted curve for direct use in material calibration workflows.
When to use¶
Classification: process.
Tagged: curve_fit, hardening, hockett, mat_24, material_calibration, mhs, neural_network, stoughton.
Inputs¶
| Label | ID | Type | Default | Required | Description |
|---|---|---|---|---|---|
| HC-ESVES | hardening_curve-effective_strainvs_effective_stress | vector | — | Input strain-hardening curve as an (effective strain, effective stress) vector pair (dimensionless vs. MPa or consistent stress units); the curve to be fitted — optional if a default test dataset is preloaded. | |
| Type Of Fit | typeof_fit | list | swift | Hardening law to fit: ‘swift’ (power-law), ‘voce’ (saturation), ‘hockett’ (Hockett-Sherby), ‘stoughton’, or ‘mhs’ (mixed hardening); defaults to ‘swift’. | |
| Iterations | iterations-higherthebetter | list | 10 | Maximum number of neural-network training iterations (1–1000); higher values improve fit accuracy at the cost of compute time — default of 10 is suitable for quick checks, use 250–1000 for final calibration. | |
| LR-LTB | learning_rate-lowerthebetter | list | 0.1 | Gradient-descent learning rate for the neural-network optimiser (1 down to 1e-5); lower values yield more stable convergence — default of 0.1 is a reasonable starting point, reduce to 0.001 or below if the loss oscillates. | |
| T-LTB | tolerance-lowerthebetter | list | 1e-05 | Lower the better | |
| Number Of Evaluations | numberof_evaluations-percentageof_points-lowerthefaster | list | 10 | Percentage of Points - Lower the faster | |
| Number Of Digitization Points | numberof_digitization_points | list | 100 | Number of Digitization Points | |
| Return Type | return_type | list | curve |
Outputs¶
| Label | ID | Type | Description |
|---|---|---|---|
| curve_fit_hardening_neuralnetwork_output_1 | curve_fit_hardening_neuralnetwork_output_1 | vector | Fitted hardening curve as an (effective strain, effective stress) vector evaluated with the optimised analytical-model parameters; ready for downstream MAT_24 keyword authoring or further material-calibration workers. |
Disciplines¶
- ai_ml.supervised.regression
- data.curve.transform
- engineering.material.calibration
- engineering.material.characterization
Runnable example¶
A runnable example is registered for this worker. Open the example workflow on the d3VIEW canvas: /api/workflow/example?id=curve_fit_hardening_neuralnetwork
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