Hybrid/tandem laser-arc welding of thick low carbon martensitic stainless steel plates for hydraulic turbines application
Bibliographic record
Abstract
The main objective of this research work was to understand the different challenges related to hybrid laser-arc welding (HLAW) of thick gauge section assemblies of low carbon 13%Cr-4%Ni martensitic stainless steel and develop a practical solution by adapting and optimizing this relatively new welding process in order to attain higher processing efficiency through a reduction in the number of welding passes necessary to fill the groove gap. Also a special focus was given to the development of the hybrid and tandem laser-arc welding techniques for the root pass. In this study, the processing methodology using the hybrid/tandem laser-arc welding technology was also adapted based on the thickness of the low carbon martensitic stainless steel plates, namely a single pass HLAW process for a 10-mm thick section and a multi-pass hybrid/tandem laser-arc welding process for a 25-mm thick section. After welding, the joint integrity was evaluated in terms of microstructure, defects and mechanical properties in both the as-welded and post-weld tempered conditions. The effect of different welding speeds on the as-welded joint integrity of the 10-mm thick and 25-mm thick assemblies was characterized in terms of the weld bead geometry, defects, microstructure, hardness, ultimate tensile strength and impact energy. Significant defects such as porosity, root humping, underfill and excessive penetration were observed at a low welding speed of 0.5 m/min. However the welds met the specifications of ISO 12932 at a speed higher than 0.75 m/min. The ultimate tensile strength and Charpy impact energy values of the fully penetrated welds in the tempered condition were acceptable according to ASTM, ASME and industrial specifications, which show good potential for introducing hybrid/tandem laser-arc welding technology for the manufacturing of next generation hydroelectric turbine components.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".