Residual stress management: Recent advances in engineering methods for non-destructive measurement and beneficial redistribution of residual stresses
Bibliographic record
Abstract
Welding is used widely in the automotive industry for joining a variety of structural components and parts. Of paramount importance in these structures are their engineering properties, such as fatigue life, distortions, dimensional stability and corrosion resistance that can be affected considerably by the presence of residual stresses (RS). The knowledge of RS and the ability to control their distribution in welded structures is critical when evaluating their fatigue life and preventing catastrophic failures. An engineering concept of residual stress management (RSM) has been developed that addresses all aspects of residual stresses in structural elements. RSM includes three major stages in stress management, i.e. RS determination, RS analysis and RS redistribution. Using this approach, stresses in structures and materials can be evaluated in each specific case either theoretically or experimentally and the performance and fatigue behavior of such structures optimized. This paper is built as an overview of the RSM concept and its application in the fields of non-destructive measurement of residual and applied stresses and in treatment of structures with residual stresses to achieve better performance and longer fatigue life. All three stages of the RSM concept will be discussed and for each of the stages practical engineering approach examples will be given. An example of a project in which the residual stress distribution in a filet welded joint was measured, analyzed and changed by post-weld treatment will be presented to demonstrate the effectiveness of the RSM approach. The advancements in the modern tools used in this project for non-destructive measurement of residual stresses using an ultrasonic computerized complex for residual stress measurement and the ultrasonic peening for redistribution of the residual stresses that allowed improving the quality of the welds and increasing their fatigue life will also be presented.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".