Ball Indentation Technique to Estimate Fracture Toughness of High-Strength Rail Steels
Bibliographic record
Abstract
In this work, the non-destructive ball indentation technique is applied to estimate fracture toughness for three types of high-strength rail steels based on continuum damage mechanics. Damage parameter, in terms of the deterioration of elastic modulus, is measured for three rail steels using the loading-unloading smooth tensile test, based on which a ductile damage model is calibrated to determine the critical damage parameter at the onset of fracture. Meanwhile, an instrumented ball indentation test is conducted on the three rail steels to generate damage as a function of contact depth under indentation compression. The critical damage parameter from the smooth specimen is then applied to the indentation test to determine the critical contact depth for calculating the indentation fracture toughness based on the concept of indentation energy to fracture. Results show that although the magnitude of the so-determined indentation fracture toughness is greater than that of the corresponding mode I critical stress intensity factor (KIc) measured using the pre-cracked single-edge-notched bend (SENB) specimen, the former can well predict the ranking order of the KIc values among the three rail steels.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".