On Bending Performance of Additively Manufactured Steel Catenary Riser (SCR): Effect of Welding Residual Stress on Bending Strain Capacity
Bibliographic record
Abstract
Additive manufacturing (AM) also known as 3D printing is defined as a bottom-up layer on layer process of joining materials to make objects from 3D CAD models. Of particular interest in this paper is a powder bed fusion technique, namely Direct Metal Laser Sintering (DMLS) method to sinter metal powders. The advantages of metal 3D printing, e.g. high strength-to-density ratio, rapid prototyping, etc. are the motivations to employ this new disruptive technology in the marine sector, besides its current applications in medical, defense, aerospace, and automotive industries. The current study is part of a series of collaborative work initiated by Marine Additive Manufacturing Center of Excellence (MAMCE) in which the bending strain capacity of two welded linepipes at the most critical failure point of SCR (i.e. touch-down zone) was examined. This paper comprises two main sections; in the first part a continuum finite element model was developed to simulate welding induced residual stresses and the results were calibrated with existing data in published literature; and the last part is dedicated to examination of bending strain capacity of the welded pipe. The methodology is used for two different model; a conventional stainless steel pipe; and a hybrid (i.e. Maraging steel-stainless steel) riser made using 3D metal printing technique in existence of welding induced residual stresses (i.e. section one). A comparison between the hybrid SCR model and conventional carbon steel one under similar conditions was presented providing valuable perspective over bending performance of each kind. The major outcomes of this paper are the residual stress pattern and bending moment-curvature graphs for both types of the pipe configurations, which comparatively demonstrate the significance of the type of manufacturing (AM and conventional methods), and existence of welding residual stress and internal pressure on bending behavior of SCR.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".