Single Point Incremental Forming Limits Using a Boxbehnken Design of Experiment
Bibliographic record
Abstract
Single Point Incremental Forming (SPIF) is a new method of forming sheet metal for which not all forming limits and forming parameters are yet completely understood. In this paper, a Box-Behnken design of experiment (DOE) is used to execute an experimental study used to determine the forming limits in Single Point Incremental Forming (SPIF). The Box-Behnken allows for good accuracy in defining a surface response for a relatively low number of experimental runs – hence its usefulness in experimental work. The Box-Behnken used in this paper solved five factors at three levels in forty six runs. The five factors analyzed are based on the most critical factors effecting SPIF; they are material type, material thickness, formed shape, tool size and incremental step size (depth of each step in form). The data resulting from the Box-Behnken progressed into graphical response surfaces; the response surfaces allow designers to determine what factors they need to select in order to successfully form a part using SPIF.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".