Estimation of the pressing force in blade forming application
Bibliographic record
Abstract
We develop a 3-D FE model to simulate the hot forming process for the turbine blades based on elastic-plastic theory and unilateral contact friction theory under isothermal assumption. Due to the quasi-static assumption, an explicit dynamic formulation is used with a scale mass matrix. A method is proposed to estimate from experimental data the temperature of the forming simulation. The evolution of the material properties versus the temperature is selected combining experimental results and bibliographic sources. The numerical model is validated using experimental data. A numerical analysis of the influence of blade size (thickness, width, length, depth) on the pressing force is described. Finally a fast model to estimate the required pressing force is proposed. A multi-input single-output model is used where the input are only defined by the geometrical parameters, the material and the temperature of the blade and the output is the pressing force. The model is approximated with a least square method based on FE simulation results. Comparisons are made between the fast and FE models.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".