Towards the Determination of Machining Allowances and Surface Roughness of 3D-Printed Parts Subjected to Abrasive Flow Machining
Why this work is in the frame
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Bibliographic record
Abstract
Abrasive flow machining (AFM) is considered as one of the best-suited techniques for surface finishing of laser powder bed fused (LPBF) parts. In order to determine the AFM-related allowances to be applied during the design of LPBF parts, a numerical tool allowing to predict the material removal and the surface roughness of these parts as a function of the AFM conditions is developed. This numerical tool is based on the use of a simplified viscoelastic non-Newtonian medium flow model and calibrated using specially designed artifacts containing four planar surfaces with different surface roughnesses to account for the build orientation dependence of the surface finish of LPBF parts. The model calibration allows the determination of the abrasive medium-polished part slip coefficient, the fluid relaxation time and the abrading (Preston) coefficient, as well as of the surface roughness evolution as a function of the material removal. For model validation, LPBF parts printed from the same material as the calibration artifacts, but having a relatively complex tubular geometry, were polished using the same abrasive medium. The average discrepancy between the calculated and experimental material removal and surface roughness values did not exceed 25%, which is deemed acceptable for real-case applications. A practical application of the numerical tool developed was demonstrated using the predicted AFM allowances for the generation of a compensated computer-aided design (CAD) model of the part to be printed.
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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 | 0.001 | 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) | 0.000 | 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 it