A Constitutive Model Fitting Methodology for Ductile Metals Using Cold Upsetting Tests and Numeric Optimization Techniques
Bibliographic record
Abstract
This work documents the development of a tool to perform automated parameter fitting of constitutive material models. Specific to this work is the fitting of a Swift hardening rule and isotropic linear plasticity model to aluminum 2024-T351, C36000 brass, and C10100 copper. Material characterization was conducted through the use of compressive, cold upsetting tests. A noncontact, optical displacement measurement system was applied to measure the axial and radial deformation of the test specimens. Nonlinear optimization techniques were then applied to tune a finite element model to match experimental results through the optimization of material model parameters as well as frictional coefficient. The result is a system, which can determine constitutive model parameters rapidly and without user interaction. While this tool provided material parameters for each material and model tested, the quality of the fit varied depending on how appropriate the constitutive model was to the material's actual plastic behavior. Aluminum's behavior proved to be an excellent match to the Swift hardening rule while the behavior of brass and copper was described better by the linear plasticity model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".