An Experimental Comparison of Weld-Induced Residual Stresses Using Different Stainless Steel Filler Metals Commonly Used for Hydraulic Turbines Manufacturing and Repair
Bibliographic record
Abstract
CA6NM and UNS S41500 martensitic stainless steels are widely used for manufacturing and repair of hydraulic turbine runners. They offer good mechanical properties and superior cavitation resistance when compared to mild steels. They are also relatively easy to weld. However, when welded homogeneously, they require a post-weld heat treatment (PWHT) in order to temper the as-welded martensite. This PWHT is also beneficial for residual stresses reduction as it effectively lowers the stress peaks. To avoid this PWHT, austenitic filler metals are often used for repair. But omitting PWHT inevitably leaves weld-induced residual stresses in the assembly. In order to better understand the impact of the weld filler metal choice on the importance of residual stress, an experimental study has been conducted on three different filler alloys. The chosen alloys were: • 410NiMo, a martensitic grade having the same composition as the base metal (13%Cr-4%Ni-0.5%Mo) ; • 309L, an austenitic grade widely used for repair (24%Cr-13%Ni) ; • A proprietary low transformation temperature (LTT) martensitic grade (13%Cr-6%Ni). This paper compares residual stresses in the as-welded condition on welds of UNS S41500 (13%Cr-4%Ni) made using these filler metals. Residual stresses were measured using the contour method. Microstructural analysis was performed to identify the phases in the weld and the heat-affected zones (HAZ). Microhardness maps were done to see the hardness distribution of each weldment.
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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 | 0.001 |
| 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".