Effects of Microstructure, Mechanical and Physical Properties on Machinability of Graphite Cast Irons
Bibliographic record
Abstract
Flake (FGI) and spheroidal (SGI) graphite cast irons are often used to produce workpieces, which often need to be machined. Machinability differences under various machining methods are the basis for choosing machining equipment and technology. In this work, FGI and SGI were used to produce tractor front brackets, and the machinability of both materials under turning and drilling processes was compared. The machinability (turning and drilling ability) has been evaluated in terms of machining load, chips shape, surface roughness, and tool temperature. The influence of materials microstructure and thermal conductivity on the machinability was analyzed. In the turning process, the cutting force and its standard deviation of the FGI were larger than the SGI due to the higher volume fraction of pearlite. The surface roughness was similar in both materials. In the drilling process, the even action of the friction and cutting force on the bit turned into similar drilling loads for both materials. Higher friction and lower thermal conductivity caused a higher bit temperature in SGI drilling compared to FGI. The chip breaking was worse in SGI drilling, where the longer chips scratched the internal surface of the holes, resulting in the higher surface roughness.
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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) | 0.000 | 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".