Experimental and numerical analyses of residual stress redistributions in large steel dies: Influence of tempering cycles and rough milling
Bibliographic record
Abstract
The aim of the present research was to study the residual stress variations induced by heat treatment and rough milling operations of large forged steel blocks. The heat treatment process includes quenching and tempering operations. An efficient strategy based on a size sensitivity analysis (SSA) was proposed to reach the actual values of residual stresses in large-size steel blocks by using small-size workpieces in laboratory scale without damaging the blocks. The residual stresses were measured and compared after the first and second tempering and the rough milling. A 3D finite element (FE) model was developed to predict superficial residual stresses and was then calibrated experimentally. The results showed that the residual stresses after the first and second tempering were highly compressive on and near the surface. In addition, the resultants of the residual stresses after double tempering processes were almost equal. Therefore, the second tempering could be omitted from the manufacturing process. It was also found that both surface and sub-surface hoop and radial residual stresses were highly tensile after the rough milling. Finally, the 3D FE model can be used as a predictive tool to predict residual stresses for rough milling to avoid conducting expensive, time-consuming experimentations and measurements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".