The Effect of LFC Process Variables on Solidification and Thermal Response of AZ91E Magnesium Alloy Castings
Bibliographic record
Abstract
Magnesium alloys are gaining in popularity as materials of choice for automotive and aerospace applications. Magnesium alloys have the lowest density of all structural metals, effectively making their specific properties highly attractive. Lost Foam Casting (LFC) is a novel near-net-shape manufacturing process utilizing expanded polystyrene (EPS) as a mold filler. Presence of the EPS in the casting cavity promotes formation of unique casting defects.These include misruns, folds, entrapped polystyrene pyrolysis products and potentially increased levels of gas porosity. There is very little published literature on the feasibility of casting magnesium alloys by the LFC process. This research was an attempt to evaluate the effect of selected LFC process variables on AZ91R magnesium alloy castings produced by the LFC process. In this work, the effect of melt superheat, casting section thickness, EPS foam properties and the application of vacuum during mold filling were investigated and correlated to the casting quality and molten flow behavior. Further, detailed thermal analysis was carried out to determine the solidification history of the castings. The results of the thermal analysis were used to determine the effect of the cooling rate on the development of the casting microstructure. Moreover, the morphology and the mode of second phase (Mg17Al12) precipitation were studied and quantified. The results suggest that application of vacuum during the mold filling process increased the metal flow lengths. However, the casting soundness deteriorated due to the applied vacuum. Variations in the density of the vacuum cast horizontal bars were explained through the presence of partially solidified metal. The molten metal flow was further influenced by the foam density and bead fusion. Greater flow lengths were observed in the high density 1.6 pcf foam castings. in the low density 1.3 pcf foam castings, numerous casting defects were associated with the presence of the liquid-EPS pyrolysis products. In general, the thermal analysis suggested that non-equilibrium alloy solidification promoted the formation of the lamellar non-equilibrium Mg17Al12 precipitate, and this was confirmed by optical microscopy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".