A Macro-Micro Model of Fusion Zone Microstructure Evolution in Mn-C Low-Alloy Steel Coupled With Thermal Stress Analysis
Bibliographic record
Abstract
A macro-micro-model for microstructure evolution in the fusion zone of a l.2 Mn and 0.11 C low-alloy steel is described. The macro-model is a 3D transient thermal analysis of a welded structure that resolves the weld pool with element size greater than 1 mm and time steps greater than 1 second. The micro-model has cell size of about 1 micron and time step size of about 10 micro-seconds with a grid of about 80×80×500 cells. The micro model is positioned on the liquid-solid interface of the weld pool in the macro-model. The boundary conditions for the micro-model are mapped from the macro-model. The micro-model solves the 3D transient solute diffusion equations for Mn and C. The micro-model computes the liquid-solid interface movement with local velocities determined by local temperature, compositions of solid and liquid phases and interface curvature to predict columnar or dendritic solidification structures. As the solid cools from the melting point to room temperature, the evolution of austenite, ferrite, pearlite, bainite and martensite phases are computed. The 3D transient stress due to temperature and phase changes is computed in the micro-model as it cools from the melting temperature to room temperature. At room temperature a micro-model tensile test is run to 4% strain. The macro-stress and strain is compared to the micro-stress and strain distributions. The model is intended to be used to initialize models of fracture, fatigue and creep in weld fusion zones.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".