Residual Stress in Engineering Materials: A Review
Bibliographic record
Abstract
The accurate determination of residual stresses has a crucial role in understanding the complex interactions between microstructure, mechanical state, mode(s) of failure, and structural integrity. Moreover, the residual stress management concept contributes to industrial applications, aiming to improve the product's service performance and life cycle. In this regard, the industry requests rapid, efficient, and modern methods to identify and control the residual stress state. This review article contains three main sections. The first section covers different residual stress determination methods and reports the advancements over the recent decade. The second section includes the role of residual stresses in the performance of a broad range of materials including metallic alloys, polymers, ceramics, composites, and biomaterials. This is presented by classifying different science areas dealing with residual stresses into two main groups, including “origins” and “effects” of residual stresses. The range of topics covered are “welding, machining, curing/cooling, and spray coating processes,” “medical and dental sciences,” and “fatigue and fracture mechanisms.” The third section summarizes various strategies to effectively control residual stresses through different manufacturing procedures. It is hoped that the data provided herein serves as a valuable up‐to‐date reference for engineers and scientists in the field of residual stress.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".