Milling characteristics of SiCp/2024Al composites thin-walled part based on movable auxiliary support
Bibliographic record
Abstract
Due to milling force and milling heat, thin-walled parts are highly susceptible to deformation and even scrap. In this paper, a method of milling thin-walled part based on the movable auxiliary support was proposed. The ABAQUS software was employed to simulate the milling of thin-walled SiCp/2024Al composite part, while the movement of the auxiliary support was simulated using the secondary development subroutine VDLOAD. The influences of the magnitude and area of the movable auxiliary support on the surface temperature, deformation, and residual stress of the workpiece were investigated by single factor method. The results showed that, with the increasing of the magnitude and area of the movable auxiliary support, both surface temperature and deformation of the thin-walled part decrease. The tensile residual stress decreases and gradually changes to compressive residual stress. Interestingly, when the magnitude exceeds 20 MPa or the area exceeds 100 mm2, the deformation of the thin-walled part continues to decrease, but the middle of the thin-walled part is concave toward the milling cutter. It is worth noting that the effect of area on the residual stress does not show a clear regularity. Thus, the reasonable auxiliary support can reduce deformation and stability in the milling of thin-walled parts and the results of the study can provide a theoretical basis for optimizing the milling process of thin-walled parts.
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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".