Influence of Flux Agent Composition in A-TIG Welding Of Cu-ETP Sheets
Bibliographic record
Abstract
Due to its high coefficient of thermal expansion and high thermal conductivity, TIG welding of copper and copper alloys is considered difficult and demanding.Activated TIG welding (A-TIG) has been widely researched as a welding method for obtaining deeper penetration on various metals.However, A-TIG welding of copper remains unexplored without known influence of flux substances, applied parameters and achieved mechanical properties in produced welds.Three substances were examined as single component fluxes in this work: Calcium oxide (CaO), Silicon dioxide (SiO2) and Sodium carbonate (Na2CO3).Produced welds were characterized by visual inspection and macrostructural analysis, also bend testing and tensile strength testing were performed.Microstructure of the weld with optimal mechanical properties was analyzed by means of Field Emission Gun-Scanning Electron Microscope (FEG-SEM) and also the chemical characterization of the weld was made by means of Energy Dispersive Spectroscopy (EDS).The results showed that the most effective flux used was Silicon dioxide.A-TIG welding with SiO2 flux enabled good results in visual inspection, macrostructure analysis, band test and tensile strength test.However, oxide inclusions have been found in weld metal in significant percentage which could indicate the possible degradation of electrical conductivity of the Cu-ETP sheets.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".