Research on Four-Point Air Bending Process and Contour Detection Method for JCO Forming Process of LSAW Pipes
Bibliographic record
Abstract
Aiming at the forming efficiency and roundness of the longitudinal submerged arc welded (LSAW) pipes in JCO (J-shape to C-shape to O-shape) forming process, this paper proposes a four-point air bending process. Compared with the traditional three-point air bending process, The new process can provide a more uniform bending moment, does not need to crimp the edges of steel sheet, shorten the residual straight segment length, and lengthen the forming length in single pass. The mechanical model is established to analyze the static equilibrium conditions and elastic–plastic deformation. The process is simulated by using the software package ABAQUS, to find the maximum punch spacing, and further determine the formulation principles of other process parameters. In addition, a contour detection method for the LSAW pipes in forming process is proposed based on machine vision (planar-array CCD camera produced by Gray Point Corporation, Vancouver, Canada). This method can not only quickly detect the contour of each pass, but also splice the detected contours together to obtain the overall contour with the given splicing algorithm. According to the measured contour, the bending angle, radius, and roundness can be calculated, to correct the punch reduction in the next pass and improve the forming accuracy of the pipes. Finally, an experimental system is designed to verify the proposed four-point bending JCO forming process and contour detection method. The result shows that the error between the contour detection method and CMM (coordinate measuring machine) is less than 0.5% for the overall contour, the two experimental pipes require 13 and 15 passes respectively, the roundness of pipes are less than 1.1%, which is much better than that of traditional three-point bending JCO forming process.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".