Virtual Surface Roughness Measurements From an ‘As-Built’ Virtual CAD Model for Bead Based Deposition Additive Manufactured Components
Bibliographic record
Abstract
Abstract All additive manufacturing processes have a characteristic ‘staircase’ layering effect at the boundaries, as this process family fabricates components by stacking layers upon each other. This effect is noticeable at shallow angles and where there is significant surface curvature. Measuring the surface roughness from virtually modeled beads of an additive manufactured product helps to have an initial estimation of the surface quality during process planning. In this paper, three techniques are developed to measure the profile surface roughness from an ‘as-built’ CAD model generated from theoretical bead geometries. Two CAD files are needed as inputs: a model of the ideal part geometry and the model created based on the bead geometry, percent bead overlap, and the fill strategy. Developed solutions are projection, projection normal-line distance, and elongation method. The results are verified by analytical calculations with less than one percent variation. The samples include flat faces, a curved surface, and an S shape (a double arc). Sensitivity studies for evaluation length are conducted as well. Estimation of the surface roughness values before a component is being built will help designers to evaluate how much material stock is needed to be added if subsequent machining processes are required. Therefore, it is anticipated that this research will assist process planners in developing their desired build solutions for both AM and hybrid manufacturing.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".