Study of non-metallic inclusions composition in rail joints welded seams, obtained at their contact arc welding
Bibliographic record
Abstract
The quality of welded rail joints depends on various factors, in particular, on the type and quantity of non-metallic inclusions, formed at their contact arc welding. To check a hypothesis of the cause-effect of welded joint mechanical properties decrease and composition of non-metallic inclusions in the welded seams, their chemical and metallographic analysis accomplished. To study the non-metallic inclusions, samples of 90×30×10 mm dimensions used, cut of Э76ХФ steel rail head. After contact butt welding at MC-2008 machine, the samples were cut by electro-erosion method perpendicularly to the welding seam into two parts. One part of the sample was used for tensile test, the other – for metallographic analysis for non-metallic inclusions and structure of the welded joint. The microstructure of the welded butts studied after milling and intensive etching in the 50% water solution of hydrochloric acid. The microstructure was studied at the optical microscope OLYMPUS GX71 in the light field at magnification 100–1000 folds after etching in an alcohol solution of nitric acid. The chemical composition of all the revealed inclusions were determined at the scanning electron microscope MIRA 3. It was established, that silicon and manganese oxides were the basic components of inclusions, revealed in the welded seam, as well as oxides of aluminum, iron, titanium, chrome – to a less amount. In the samples seam microstructure the ferrite net, typical for rail butts, was not discovered, which is probably stipulated by their accelerated heating due to the small section of the samples.
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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 | 0.001 | 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".