Fracture Behavior Prediction of a High-Strength Aluminum Alloy under Multiaxial Loading
Bibliographic record
Abstract
This study presents a triaxiality analysis and fracture behavior prediction of a high-strength aluminum alloy, specifically AW5754, under multiaxial loading conditions.The primary objective is to obtain a triaxiality locus, which depicts the relationship between the equivalent plastic strain to fracture and the stress triaxiality factor.This locus provides comprehensive insights into the fracture behavior of the material under various stress states.Experimental tests employing various specimen geometries are conducted to acquire essential data for analysis and to facilitate the development of a finite element (FE) model.Quasi-static uniaxial tensile tests are performed on five different specimen types, and accurate deformation measurements are obtained using extensometers at critical locations.The simulation results from the FE models are then compared with the experimental measurements to ensure their accuracy.The developed FE models are used to calculate the equivalent fracture strain and stress triaxiality factor with the help of collected test data.These calculations enable the generation of a stress triaxiality locus through a curve-fitting process.An exponential curve fitting function is chosen to appropriately relate the equivalent plastic strain to the fracture and stress state for the AW5754 aluminum alloy.The resulting stress triaxiality locus serves as a valuable tool for predicting fracture strain and evaluating stress states more accurately.The outcomes of this study contribute significantly to our understanding of the fracture behavior exhibited by high-strength aluminum alloys under multiaxial loading conditions.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".