Can a Weld in Welded Structure Be Made With Zero Residual Stress?
Bibliographic record
Abstract
While solving a sequence of seventeen optimization projects to predict the values of the side heater parameters that would be expected to minimize camber distortion in an edge welded bar, the design parameters that reduced distortion to effectively zero were not unique. This raised the question if any of the designs that minimized the distortion effectively to zero also minimized the residual stress. To answer this question three different measures of residual stress were evaluated for all 1451 designs. The Computational Weld Mechanics (CWM) optimization problem is to find the best point in the 4D space of side heater design parameters: flux, heated area, longitudinal and transverse distance from the weld such that the final residual stress is as low as possible (minimized). To evaluate the objective function for each point in the 4D design space, the associated 3D transient non-linear thermal visco-elastic-plastic stress analyzes was solved. A FEM mesh with 6600 8-node brick elements and 9438 nodes was solved for 166 time steps in 10 minutes of single-core CPU time. In the seventeen optimization projects, 1451 weld analyses were solved in 75 quad-core CPU hours by one person in two calendar weeks. The residual stress was effectively reduced to zero in some designs. These designs also reduced distortion to effectively zero. Whether a design that effectively reduces the residual stress to zero is unique remains an open question.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".