Crystal plasticity analysis of instrumented indentation on a service-aged Cr–Mo steel
Bibliographic record
Abstract
Samples extracted from pressure vessels that had been in service for 40 years at 480 °C revealed a 24% increase in tensile strength and a 30% decrease in elongation compared with new material. However, artificially aging at 700 °C for 2 h revealed a decrease in tensile strength and a slight decrease in elongation despite having a similar Holloman–Jaffe (HJ) tempering parameter to the service-exposed samples. Mesoscale crystal plasticity simulations were conducted to understand the aging microstructure on microhardness indentation, incorporating a dislocation density-based constitutive law. Experimental results in dislocation density, grain orientations, and particle sizes were used as inputs for the crystal plasticity model. Atomic force microscopy (AFM) was used to measure the depth profile of the nanoindentation marks, validating the model. Both experimental and simulation results indicated that precipitation hardening by nanoscale semi-coherent M o 2 C significantly strengthened the ferrite matrix during aging. The dissolution of F e 3 C in pearlite had decreased the tensile strength, while the increased carbide precipitation and dislocation densities had increased the tensile strength of the ferrite matrix in the service-exposed samples. An indentation model for microhardness of the pearlite regions confirmed that the spheroidization of carbides in pearlite during aging had contributed to the decrease in hardness and strength.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".