Residual Stress Modeling of Induction-Bent Pipes
Bibliographic record
Abstract
The induction bending process using local induction heating is widely used to produce large diameter pipes with relatively small bend radii at low tooling cost. This process was considered for the fabrication of stainless steel feeder pipes for CANDU® reactors instead of cold and warm bending processes. Wall thickness measurements were performed, before and after bending, using an ultrasonic testing method on a number of test bends fabricated with this induction bending process. Residual stress measurements have been performed on a test bend by X-ray diffraction. A numerical model has been developed using LS-DYNA® to predict the residual stress and the deformed shape of these bends. The numerical model has also been used to study the effect of some key input parameters, such as bending speed, bending temperature, process parameters of induction heating and cooling, and the yield strength of the feeder material. This information can be used to improve the bending process such that lower residual stress and more uniform wall thickness can be achieved. In this paper, the simulation work is summarized and the comparison between the simulation results and the measurement data are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".