Enhancement of material properties of SS 316 L base metal by TIG cladding process
Bibliographic record
Abstract
The Tungsten Inert Gas (TIG) welding process was utilized to apply stainless steel (SS) 304 filler cladding onto the stainless steel (SS) 316 L base metal. This cladding resulted in an enhancement of the base metal's properties. An optical study and scanning electron microscope (SEM) analysis conducted to investigate the metallurgical properties revealed refined grain structures, indicating improved deposition of the SS 304 filler. The optical survey classified the different zones as base metal, weld metal, and transition line, with the transition line distinguishing the deposition of the filler metal through the cladding process. The refined grain structures, optimal process parameter selection (current, gas flow rate, and travel speed at a minimal level), and double-pass cladding have significantly influenced microhardness and tensile test values. The cladding of SS 304 fillers on SS 316 L alloys demonstrated improvement in the microhardness and tensile properties by 42.5 % and 13.2 %, respectively. Further investigation into the nozzle gun's heat input, current, and travel speed showed that maintaining a consistent travel speed with a steady heat source had a substantial impact on the material properties of the base metal.
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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".