Influence of deformation path on the stress state and damage evolution along the central axis of a large size forged ingot of AISI H13 steel
Bibliographic record
Abstract
Development of cracks along the center axis of large high strength steel bars commonly occurs during the forging and leads to excessive part rejections. The present investigation aims to develop a better understanding of the evolution of stress-strain states during the forging operation and in particular the effect of deformation path illustrated by die geometry, on the evolution of damage during the cogging of an AISI H13 steel. Hot compression and tensile tests were performed using Gleeble-3800 thermo-mechanical simulator to develop the optimum material model which was then implemented in the finite element (FE) code Forge NxT 3.2® using a developed user subroutine. Normalized Cockcroft and Latham damage criterion and maximum shear stress (Tresca's) theory of failure were used to predict the damage and failure in the center axis of the shaft through FE analysis with three different die shapes: concave, flat, and convex. A comparative study between the three die geometries was conducted to quantify the effects of each of them on the sensitivity to central burst damage. FE model was validated using industrial data. The lowest and highest damage values were found to occur in the case of cogging with concave and flat die, respectively. The coefficient of variation (CoV) is employed as a measure of heterogeneity and it was found that the concave die provides more uniform deformation and most favorable results for the cogging compared to the flat and convex dies. The novel approach, application of concave die successfully implemented at the industrial scale cogging.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".