Effects of Welding Processes and Techniques on Mechanical and Metallurgical Properties of Dissimilar Metal Weld
Bibliographic record
Abstract
Abstract The dissimilar metal weld (DMW) is widely used in fabrication and manufacturing in various industries. Joining between nickel-based alloy and ferritic steel tubing and piping is commonly employed for ASME Code compliant welds for high-temperature and corrosion resistance applications. A series of DMW samples between alloy 600 pipe and SA-106 Grade B pipe are fabricated using different welding processes, joint design and welding techniques. By detailed comparison, this paper provides insight into the effects of these different welding variables on mechanical properties (tensile properties and hardness of weld materials and heat affected zone), metallurgical properties (macro and microstructure examination) and chemistry (root pass alloying dilution etc.) It has been shown that an asymmetric joint bevel design in consideration of different heat dissipation, melting temperature of the two materials will promote good weld bead formation during the root pass welding. Different joint designs (such as with or without consumable insert) will create variations on weld dilution and Cr/Ni recovery in the root area. Other welding variables such as tungsten electrode location for root pass welding for DMW, machine Gas Tungsten Arc Welding (GTAW) using hot wire and cold wire, etc. are also discussed.
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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 | 0.001 |
| 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".