Cast joining of cast iron to aluminium casting matrix
Bibliographic record
Abstract
The present study focused on reinforcing Al–Si–Cu alloy with a cast iron insert and using Zn– xAl–3Si–0·5Mg (wt-) intermediate alloys by the cast joining technique to take advantage of lightness and stiffness of the hybrid structure. The experimental set-up consisted of coating the insert using hot dipping method followed by immersing the coated insert into the Al melt and allowing the system to cool down to the room temperature. The quality of Al–Fe joints in terms of morphology, thickness, chemistry and microhardness was evaluated as a function of coating composition and immersion time in the Al melt. Characteristics of reaction layer at the coating/insert interface and its effects on the joint properties were determined using microstructural analysis and thermodynamic calculations. Combination of a suitable coating containing 27 wt-Al and optimised process parameters, including 1 min immersion time, resulted in the formation of an Al–Fe joint with promising characteristics.
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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 | 0.000 | 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) | 0.002 | 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".