Sensitivity of Hot Cracking to Weld Procedure Parameters and Transverse Traction
Bibliographic record
Abstract
The paper focuses on hot cracking susceptibility analysis and a post-processor for a computational weld mechanics (CWM) framework to identify the transient 3D region susceptible to hot cracking for a welded structure. The Sigmajig hot cracking analysis analyzed by Zacharia in [1] is used. The specimen is 50 × 25 × 0.25 mm 316 stainless steel sheet welded with a TIG process with a constant transverse force applied on the side surfaces. The tensile traction, welding power, welding speed, and 4 double ellipsoid shape parameters are varied in a sensitivity analysis of hot cracking wrt the transverse tensile traction and several welding speeds for which power per unit length is kept constant. A control problem solved to adjust 4 double ellipsoid shape parameters for different welding speeds with constant power per unit length. The analysis includes 117 analyses for the control part and 28 analyses for the sensitivity part that are implemented in an automated mode of the CWM framework to save user time in implementation. The user prepares one single base project setup and 117+28 CWM analyses by the user specifying a DOE-matrix.
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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 | 0.004 |
| 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 | 0.000 |
| 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 | 0.000 |
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".