Effects of Trace Elements on the Microstructural and Machinability Characteristics of Al–Si–Cu–Mg Castings
Bibliographic record
Abstract
This study was undertaken to emphasize the influence of Sn and Bi addition on the machinability of Sr-modified, grain-refined, and heat-treated Al–Si B319 and 396 alloys. Drilling and tapping tests were conducted to examine the cutting forces, tool life, tool wear, built-up edge evolution, and chip shape. Microstructures were examined using optical and electron microscopy. Drilling test results show that the B319.2 alloy with 0.15%Sn yields the longest drill life, i.e., twice that of the B319.2 alloy containing 0.5%Bi, and one-and-a-half times that of the B319.2 alloy containing 0.15%Sn + 0.5%Bi. The presence of 0.5%Bi in the B319.2 alloy causes a deterioration of drill life (cf., 1101 holes with 2100 holes drilled in the B319.2 alloy containing 0.15%Sn). The α-Fe phase in the 396 alloy produces the highest number of holes drilled compared with alloys containing sludge or β-Fe. The presence of sludge decreases the drill life by 50%. Built-up edge (BUE) measurements and optical photographs show little change in the BUE width for different numbers of holes except for the B319.2 alloy containing 0.5%Bi, which shows a slightly lower width (0.166 mm) compared with that containing 0.15% Sn (0.184 mm) or 0.15%Sn + 0.5%Bi (0.170 mm).
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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) | 0.001 | 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".