Surface Topography from Single Point Incremental Forming Using an Acetal Tool
Bibliographic record
Abstract
This paper investigates a new tool, where the forming tip is constructed from acetal. The acetal tip is investigated because it is self-lubricating and more compliant than traditional SPIF tools. This work characterizes the topography of surfaces created by forming aluminum with both the acetal-tipped tool and a carbide tool. When the parts are compared visually, the parts formed with the acetal tool maintain the appearance of the unformed sheet metal. The surfaces of the parts are measured using an Olympus LEXT OLS4000, a vertical scanning laser confocal microscope. Surface height as a function of lateral position on both sides of the parts (contact and free surface) is measured. These measurements are analyzed quantitatively using areal surface texture parameters and qualitatively compared with micrographs of the surfaces. Comparisons of the surfaces that are in contact with the tool reveal that the surfaces produced with the acetal tool are rougher but more isotropic than those produced using the carbide tool. The surfaces produced by the carbide tool have a more anisotropic appearance, which is created by the tool as it steps down to form the part. The benefit of using the acetal tool rather than the carbide tool is the absence of the anisotropy caused by tool step down. The free surfaces produced by both tools are much rougher than the surfaces that contact the forming tools, since the tool does not affect roughness of the free surfaces.
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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 | 0.001 |
| 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) | 0.003 | 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".