Single- and multi-objective optimization of internal gear flowforming process based on increasing tooth height and reducing force and built-up edge
Bibliographic record
Abstract
Flowforming is an incremental metal forming process in which deformation occurs gradually between the roller and mandrel. This process has been widely used for manufacturing of high-precision tubular parts and seamless tubes. Internal gear flowforming process is a new method for manufacturing an internal gear that requires no additional machining process. In this study, the design of experiment method and regression analysis were used to obtain tooth height, force, and built-up edge as functions of the effective parameters, including feed rate, roller diameter, attack angle, and thickness reduction percentage. Next, the simulated annealing and genetic algorithm were used for, respectively, single- and multi-objective optimization of the obtained functions. The goal of optimization was to reduce the force and built-up edge and increase the tooth height. In single-objective optimization, the maximum tooth height was 0.576 mm, the minimum force was 1606.63 N, and the minimum built-up edge was 0.448 mm. In multi-objective optimization, an optimal set of tooth height, force, and built-up edge (Pareto front) is obtained.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".