Through process modeling of aluminum alloy castings relating casting defects to fatigue performance
Bibliographic record
Abstract
There continues to be increasing interest in using cast aluminum alloy components in automotive applications with cyclic in-service loads. Predicting fatigue performance is a key issue in the design of these components and must consider the entire manufacturing route which typically involves casting, heat treatment and machining. A through process modeling methodology was used to predict fatigue life of one such component, an A356 automotive wheel. The technique tracks the microstructure and defect formation during the casting process as well as the residual stresses that arise due to heat treatment and subsequent finish machining. The micro structural features and the final residual stress state are used as input parameters to calculate the final cyclic stress state and in-service fatigue life. The pore size distribution and secondary dendrite arm spacing formed during casting were predicted using model-based constitutive equations run within a validated macroscopic heat flow model of the process. These constitutive equations were developed by regression fitting to results from an in-house mesoscale solidification model. The residual stresses formed during the quench stage of heat treatment and released during finish machining were simulated in a two-stage thermal stress model. A final stress/displacement model was developed to calculate the variation of the multi-axial stress state and the expected fatigue life of the wheel during cyclic in-service loading. Each of the model results shows good agreement to measurements taken at various stages of the manufacturing process. In particular, excellent agreement was attained for in-service strain. The fatigue performance was compared with full-scale fatigue test results to validate the suitability of the through process modeling for application to aluminum alloy wheels.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".