Determination of Optimal Forging Conditions for Void Elimination in Large Steel Ingots
Bibliographic record
Abstract
The presence of internal voids is commonly observed throughout the casting and solidification of large size ingots. Their mechanical closure is generally achieved during the initial deformation of a hot forming process. The present work focuses on the determination of optimal forging conditions for void elimination in large steel ingots with respect to the involved materials and industrial processes. A state of the art is compiled as initial research in order to develop a solid background in void elimination theory. An extensive review of void closure models is presented and characterisation techniques are discussed. It is shown that current void closure models lack application to industrial scale forgings. An analysis of the industrial partner’s open die forging procedure ensues and characteristic forging sequences are introduced. Feasibility for further forging analysis using experimental data is evaluated and successfully proposed. A novel method for fast calculating void closure models is developed. Rational polynomial functions are established for the calculation of material dependant constants. 3D mapping is used to evaluate the influence of the triaxiality state and material parameters. The void closure model is validated for use on high strength steels from the industrial partner. Void closure is modeled and simulated during an open die forging sequence. The effect of in-billet void positioning is studied and the forging sequence effectiveness for void closure is validated and characterized for different zones. An original combination of data from relative void closure and volumetric strain rate provides a way for forging optimisation. Novel software for successful open die slab forging, Forge Calculus, is developed based on large amounts of experimental data. The in-house code provides fundamental information for setting forging standards. Future development concerning real time prediction of forging quality is discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".