Effect of Tensile-Strain Rate on Mechanical Properties of High-Strength Q460 Steel at Elevated Temperatures
Bibliographic record
Abstract
This paper presents the effect of temperature and tensile-strain rate on the mechanical properties of high-strength low-alloy structural Q460 steel. Standard coupon tensile tests were carried out to obtain the stress-strain curves of Q460 steel subjected to a temperature range of 25°C–800°C. Three tensile-strain rates, namely, 0.001/min, 0.02/min, and 0.2/min, were selected to investigate the effect of strain rate on mechanical properties. Based on the stress-stain curves, the yield strength at different strain levels, tensile strength, and elastic modulus were determined. The reduction factors of mechanical properties of Q460 steel were calculated as the ratio of properties at elevated temperature to those at ambient temperature. The test results show that the strength and elastic modulus of Q460 steel remains 80% at temperatures lower than 500°C, and higher tensile-strain rate yields lower strength and elastic modulus properties. The reduction factors of mechanical properties decline significantly when the temperature exceeds 500°C, and the higher tensile-strain rate yields higher strength and elastic modulus properties. All the specimens experienced obvious necking before fracture and showed good ductility. Different high-strength steels exhibit different reduction factors even though the nominal strength of these steels is similar. The reduction factors suggested by other standards were not suitable to predict the properties deterioration of high-strength Q460 steels at elevated temperatures.
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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 | 0.000 | 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 | 0.001 |
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".