Robotic post-weld heat treatment for in situ repair of stainless steel turbine runners
Bibliographic record
Abstract
This paper presents a new robotic heat treatment process designed to performed in situ interventions in hydroelectric turbine runners. Considering the down-time required to dismantle a turbine unit, most utilities perform in-situ inspections and interventions to address issues such as cavitation and cracking. Repairs are primarily done by welding. Lacking a solution to perform in situ heat treatment, quality repair are impossible on modern martensitic stainless steel turbine runner. To perform on site local short duration post weld heat treatment, an induction heating system is coupled to a portable robot that can access the confined space between runner blades. The robot moves a pancake coil to inject heat and control temperature distribution to satisfy heat treatment requirements. A simulator using thermal finite element analysis is used for path planning. The system is validated on a full size Francis turbine runner blade.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".