Predictions of precipitation reaction mechanisms for 7xxx series aluminum alloys cast by CDS technique
Bibliographic record
Abstract
The need to reduce the fleet fuel consumption is pushing the automotive industry to reduce vehicles weight.In this context high strength aluminum alloys are a viable alternative to the heavier steel currently adopted.In particular 7xxx series wrought alloys, thanks to their excellent strength to weight ratio, are drawing the attention of carmakers.The development of the Controlled Diffusion Solidification (CDS) technique allows now the casting of these alloys into near net shapes, thus reducing all the costs related to the manufacturing process and making them attractive.Because of the completely different microstructure resulting from the CDS process, a new design of the heat treatments is required to achieve the best mechanical properties.This project therefore evaluates the macro and microhardness evolution of CDS cast 7xxx alloys in T4 and T6 conditions to predict their precipitation sequence, thus providing useful information for the heat treatments design.v Dedication A mia nonna, per il suo affetto; questa laurea e' anche merito tuo.Ai miei genitori, per i loro insegnamenti e incoraggiamenti, e per il nostro bellissimo rapporto.Ai miei nonni, per essere ineguagliabili e sempre disponibili.To my grandmother, for her love; this degree is even because of you.To my parents, for their teachings and encouragements, and for our beautiful relationship.To my grandparents, for being incomparable and always available.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".