Bibliographic record
Abstract
Hot tearing refers to cracks that frequently occur within the mushy zone during cooling from the liquid to solid state during shape and ingot casting. Both ferrous and non-ferrous alloys may be affected, and there is some evidence to suggest those with long freezing ranges are more susceptible. Due to the nature of this defect the economic impact is often significant and can result in an immediate productivity loss. It is therefore important for industry to be able to better predict the susceptibility of various alloys to hot tearing. Various theories have been proposed and several different types of experimental methods have been developed to interpret the properties of alloys in the semi-solid state. However, many of these techniques do not produce good quantitative data (i.e. strain) that can be used to calibrate a thermal-mechanical computer simulation of casting. Existing experimental methods often measure strain indirectly by means of a load train frozen into the end of the casting. However, local strain at the hot tear initiation site would be more valuable for computer model calibration. Clearly, the use of traditional measurement techniques, such as strain gauges, is not a viable option and therefore an alternative was investigated. In this work the use of digital image correlation to determine the evolution of strain and strain at the onset of localisation resulting in a hot tear has been evaluated. Data has been determined for aluminium alloys AA6111, AA3104, CA32118, Al-0.5% wt pct Cu under slow cooling conditions and AA3003 under directional solidification using a water cooled copper chill. A new hot tearing experiment has been developed which localises strain to promote hot tearing to occur in only one region of the casting and is cooled by directional solidification. Images of this region were captured during solidification via a glass window embedded in the mould of the experiment. These images were correlated with each other to determine strain accumulated during hot tearing using 3rd party commercial digital image correlation software.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".