Modelling of Strain-Controlled Thermomechanical Fatigue Testing of Cast AlSi7Cu3.5Mg0.15 (Mn, Zr, V) Alloy for Different Aging Conditions
Bibliographic record
Abstract
Thermomechanical fatigue loadings (TMF) applied on components in a certain temperature range with a variable state of stress (tensile and/or compression) produce a localized concentration of plastic strains that results in crack initiation and propagation. The time evolution of plastic strains must be known a priori to predict the lifetime of a part submitted to TMF loadings, which requires an extensive campaign of mechanical characterization conducted at different temperatures and aging conditions. Such a campaign was proposed for the aluminum alloy AlSi7Cu3.5Mg0.15 (Mn, Zr, V), which is recognized as being creep resistant. Combined isothermal low-cycle fatigue and isothermal creep tests were performed on this alloy to determine the constitutive parameters based on the Lemaître and Chaboche (LM&C) viscoplastic model. These laws were implemented within the finite element simulation software (Z-set) to model the response of the alloy to a thermomechanical fatigue test. The results of TMF Z-Set simulations, using the LM&C model adapted for two aging conditions, were then compared with results obtained from “Out of Phase” thermomechanical fatigue testings (OP-TMF) performed on a Gleeble 3800 machine. The modelling of the OP-TMF test revealed the complexity of the mechanical behavior of the material induced by the temperature gradient prevailing along with the cylindrical specimen. It was found that a better prediction of the evolution of plastic strains requires taking into account a larger range of plastic strain rates conditions for the determination of the constitutive law and eventually includes the role of the microstructure in the evolution of the material behavior, starting first with the yield 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".