Residual Stress in Automotive Powertrains: Methods and Analyses
Bibliographic record
Abstract
Residual stress is one of the main reasons for failure of automotive cylinder blocks and engine heads. These failures are typically associated with in-service distortion or cracking occurring in engines during operation cycles. The problem becomes more pronounced for engines that are running at elevated operating pressures and temperatures, limiting R&D options in developing and implementing higher-efficiency engines. New aluminum alloys and manufacturing methods have been introduced with varying degree of success, in many cases affected by the stress magnitudes and stress distribution in the component. Therefore, active research is ongoing internationally on finding the most reliable methods of stress analysis as a basis for developing efficient methods for stress mitigation. The current study presents a comparison between two experimental strain measurements techniques: a destructive method that is based on application of strain gauge sensors, and a non-destructive method using neutron diffraction. The results indicate that although the strain gauge method provides an indication of the nature (i.e. compression or tension) of strain within a component, this method should primarily be used for surface measurements and qualitative analyses only. Neutron diffraction remains the superior technique for strain analysis, particularly for engineering components with complex geometries. The results from this study provide the transportation industry with a more comprehensive understanding of the efficacy of utilizing strain gauge sensors, neutron diffraction or finite element modelling for measuring the residual strain in cast components. The results will help manufacturers to develop the next generation of powertrain systems with increased efficiency and improved performance.
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 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".