Failure Mechanisms of Equal and Unequal Wall Thickness Hybrid Maraging Steel-P20 Tubular Joints: Effects of Welding Residual Stresses
Bibliographic record
Abstract
Abstract Joining an additively manufactured component to a forged or cast part through welding processes has recently attracted the attention of engineers and scientists. This technique integrates the technical benefits of additive manufacturing (AM) technology with conventional processes that may be more cost-efficient. In this paper, the effect of residual stresses on the mechanical performance of a hybrid welded pipe joint connecting an additively manufactured maraging steel (MS1) pipe segment with a conventional P20 steel tube having an equivalent outside diameter was studied. A sequentially coupled thermo-mechanical continuum finite element (FE) modeling procedure to predict the residual stress state on circumferential pipe hybrid MS1-P20 joints subjected to multi-axial loads was developed and validated. Available experimental data on a welded pipe joint with conventional stainless steel (SUS304) were used to calibrate the model. The FE modeling procedures were further validated for the hybrid MS1-P20 joint. The predicted residual stress state was mapped on the pipe joint with equal and unequal wall thickness joint transitions. The mechanical performance of these pipe joints was evaluated with the application of internal pressure, uniaxial tension, and flexural loads. The major contribution of this study was the proposition of a new concept of hybrid joints, where a significant transition of the load was expected. The new hybrid joint concept was presented to meet the existing design criteria requirements without sacrificing other parameters (e.g., component weight and manufacturing expense) and facilitate the production of hybrid components using AM techniques.
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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 | 0.000 | 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".