Cooling curve and microchemical phase analysis of rapidly quenched magnesium AM60B and AE44 alloys
Bibliographic record
Abstract
Purpose: Development of the understanding of the effect of the solidification rate with the alloy microstructures for the structural AM60B and the creep resistant AE44 Mg casting alloys. Design/methodology/approach: Tubular macro test samples of magnesium alloys AM60B and AE44 were melted and quenched at maximum instantaneous cooling rates ranging from -5°C/s to -500°C/s in the Universal Metallurgical Simulator and Analyzer (UMSA) Technology Platform while recording the temperature-time traces. Such rapid cooling rates are typical in water-cooled dies used in high pressure die casting (HPDC). Characteristic reactions on these curves corresponding to the formation of individual phases during solidification were quantified based on cooling curve analysis combined with metallographic and micro-chemical analysis, with the aid of literature data. Findings: The results indicate that these phases, their size and location in the microstructure, their chemistry and their relative proportions all change in response to the increase in the cooling rate. The results are drastically different for the two alloy systems studied. Solidification of AM60B alloy yields small, equiaxed a-Mg rosettes whose size is mostly independent of the cooling rate. These rosettes nucleate heterogeneously on Al8Mn5 phases that are first to form, and are surrounded by the eutectic structure of Mg and Mg17Al12. In contrast, the AE44 has very large a-Mg grains at all cooling rates. These grains are filled with Al11RE3 platelets or dendrites. Results suggest that the Al11Re3 phase is completely ineffective in heterogeneous nucleation of a-Mg grains. Originality/value: In this research the authors significantly extended the thermal analysis methodology. The specific results obtained on the structural and creep-resistant Mg casting alloys are of significant value to the development of automotive light metal structures and power train components as well as further development of solidification codes for the commercial HPDC process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".