Usage of the contour method in measuring residual stress in welding and peen-welding applications
Bibliographic record
Abstract
Residual stresses, which are inherent to many manufacturing processes, may considerably reduce the fatigue properties of mechanical systems. Welding is a process that induces residual stresses due to plastic deformation and phase changes, which take place within the heat-affected zone (HAZ). Mechanical surface treatments are often used to minimize or even reverse the tensile stresses due to welding. In this study the influence of peening on existing welding residual stresses, through all the plate thickness, is shown using the contour method. The contour method, the measurement protocol selected in this paper for estimating residual stresses, allows the assessment of these stresses over a whole cross-section, unlike other common methods which only provide limited local point measurements. This method is based on the relaxation of stresses resulting from EDM cutting. Displacements of the relaxed cross section are measured by a laser beam. The cut surface is treated and approximated through a polynomial surface, which is used to impose displacements on the nodes of a finite element model. The solution to this problem is the normal stress responsible for the released micro-displacements on the plane section cut. This method was applied on 516 carbon steel plates either in the as-welded condition or welded and peened.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".