Analysis of Void Closure during Open Die Forging Process of Large Size Steel Ingots
Bibliographic record
Abstract
Large size forged ingots, made of high strength steel, are widely used in aerospace, transport and energy applications. The presence of internal voids in the as-cast ingot may significantly affect the mechanical properties of final products. Thus, such internal defects must be eliminated during first steps of the open die forging process. In this paper, the effect of in-billet void positioning on void closure throughout the ingot breakdown process and specifically the upsetting step in a large ingot size steel is quantitatively investigated. The developed Hansel-Spittel material model for new high strength steel is used in this study. The ingot forging process (3D simulation) was simulated with Forge NxT 1.0® according to existing industrial data. A degree of closure of ten virtual existing voids was evaluated using a semi-analytical void closure model. It is found that the upsetting process is most effective for void closure in core regions and central upper billet including certain areas within the dead metal zone (DMZ). The volumetric strain rate is determined and two types of inertial effects are observed. The dependence of void closure on accumulated equivalent deformation is calculated and discussed in relation to void in-billet locations. The original combination of information from both relative void closure and the volumetric strain rate provides a way to optimize the forging process in terms of void elimination.
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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) | 0.000 | 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".