AUSTENITIC MANGANESE STEELS – DEVELOPMENTS FOR HEAVY HAUL RAIL TRANSPORTATION
Bibliographic record
Abstract
In 1882, Sir Thomas Hadfield patented an alloy with quite remarkable properties. Its composition was Fe-1.3%C-13%Mn and it was the toughest alloy known! The claim has stuck for the century since then and the alloy is now used universally for the “frog” in railway crossings in countries such as Canada where heavy loads are moved by rail using high axle loading. In fact, the alloy is very soft when cast, but hardens rapidly when deformed.The work described has been concerned with modifying the composition of the alloy in order to trade some of the enormous toughness for improved deformation and abrasion resistance in the as-cast condition. Since these frogs are usually rebuilt by arc welding, this too was examined in the test alloys. Recommendations are made for small metallic additions, improved heat treatment and an improved welding rod composition for use in rebuilding damaged Hadfield’s steel frogs.En 1882, Sir Thomas Hadfield a breveté un alliage ayant des propriétés bien remarquables. Sa composition était Fe-1.3%C-13%Mn et c’était l’alliage le plus résilient qui soit! La revendication a tenu tout le siècle qui a suivi et l’alliage est maintenant utilisé universellement pour le coeur d’aiguillage dans les croisements de chemin de fer dans des pays comme le Canada où des charges lourdes sont déplacées par rail en utilisant une charge élevée par essieu. En fait, l’alliage est très mou lorsqu’il est coulé mais il durcit rapidement lorsqu’il est déformé.Le travail décrit concerne la modification de la composition de l’alliage afin d’échanger un peu de l’énorme résilience contre une amélioration de la déformation et de la résistance à l’abrasion sous la condition de brut de coulée. Puisque ces coeurs d’aiguillage sont habituellement remis en état par le soudage à l’arc, on a également examiné cela dans les alliages évalués. On recommande de petites additions métalliques, un traitement thermique amélioré et une composition améliorée de la baguette de soudure pour utilisation dans la remise en état de coeurs d’aiguillage endommagés en acier de Hadfield.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".