Practical Design Equations for Cold Roll Forming of Doubly Curved Hull Plates Using Line Array Roll Set
Bibliographic record
Abstract
The line array roll set (LARS) process, as one of many kinds of incremental forming processes, is a continuous process in which a flat metal plate is formed into a singly or doubly curved plate through successive passes of forming rolls. It was found that the curvature level of the formed plates in the previous study was well over the curvature required in shipyards. This fact shows that the LARS method has good potential for shipbuilding applications. A ship hull is composed of many curved plates with various double curvatures. Consequently, for the desired curvatures of target shapes, the bending radii must be determined considering the springback phenomenon. In the present study, design equations are proposed for engineering applications, particularly in relation to shipbuilding. For the development of design equations, the deformation of the plates is analyzed using some engineering assumptions. In addition, experimental coefficients are introduced to simplify the theoretical equations. The experimental coefficients are determined as the best fit for the experimental data using the least squares method, resulting in the derivation of design equations. It has been shown that the prediction from the design equation with the results from the experiments shows good agreement and can be used for practical applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".