Effect of Compliance on Residual Stresses in Manufacturing With Moving Heat Sources
Bibliographic record
Abstract
Abstract Manufacturing processes involving moving heat sources include additive manufacturing, welding, laser processing (cladding and heat treatment), machining, and grinding. These processes involve high local thermal stresses that induce plasticity and result in permanent residual stress and distortion. The residual stresses are typically calculated numerically at great computational expense despite the fact that the inelastic fraction of the domain is very small. Efforts to decouple the small plastic part from the large elastic part have led to the development of the tendon force concept. The tendon force can be predicted analytically for the case of infinitely rigid components; however, this limitation has prevented the broader use of the concept in practical applications. This work presents a rigorous mathematical treatment using dimensional analysis, asymptotics, and blending which demonstrates that the effect of geometric compliance depends on a single dimensionless group, the Okerblom number. Closed-form expressions are derived to predict the effect of compliance without the need for empirical ad-hoc fitting or calibration. The proposed expressions require input of only material properties and tabulated process parameters and are thus ideally suited for use in metamodels and design calculations, as well as incorporation into engineering codes and standards.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".