A Numerical and Experimental Assessment of the Small Punch Creep Test for 316L(N) Stainless Steels
Bibliographic record
Abstract
This paper presents a finite element analysis of the small punch creep test for 316L(N), which is compared with experimental data for 650 and 700 °C. Special emphasis is placed on (i) assessing the influence of friction and (ii) comparing two different creep models: the simple Norton creep and the more general creep model. The computed normalized deflection rate versus time is almost identical for all cases, which allows for scaling of the results. The computed time to rupture increases linearly with the friction coefficient due to a reduction in the mean stress. There is a good overall agreement between the experimental values and the computed deflection rate for a friction coefficient of around 0.3. It is shown that the initial reduction in deflection rate is due to stress relaxation and homogenization, and is only marginally affected by primary creep hardening. The computed results are compared with the equivalent stress and strain rates in the recently published small punch standard (EN 10371). The computed von Mises stresses at minimum deflection decrease linearly with the friction coefficient but are consistently slightly higher than the equivalent stress in the standard. For the strain rates, the computed values are significantly higher than the equivalent values in the standard. The presented simulations give a deeper insight of the small punch creep and impact of key parameters such the friction coefficient and in general as a guidance to refinement and improvement of the empirically based formulae in the standard.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".