Fatigue life prediction of spot-welded joints using a novel indentation technique for precise elastoplastic characterization of weld zones
Bibliographic record
Abstract
Accurately predicting the fatigue behavior of spot welds remains challenging due to varying material properties across weld zones. This study investigates the fatigue behavior of spot welds in DC04 steel through experimental testing and finite element modeling (FEM). Four modeling approaches—Solid Element (SE), MPC Beam Element (MBE), Quad RBE3, and Triangular RBE3 Elements (T-RE)—were used to simulate the weld nugget, with material properties of the base metal (BM), heat-affected zone (HAZ), and fusion zone (FZ) characterized using a novel indentation method. Fatigue life predictions were conducted using multiaxial criteria, including Morrow, Brown-Miller-Morrow (BMM), and Smith-Watson-Topper (SWT). Microhardness and microstructural analyses identified a decarburized ''pale halo line'' at the FZ/HAZ interface, resulting in a notable reduction in hardness. Experimental fatigue tests validated the numerical models, with simulations using SE providing the most accurate predictions of fatigue life and fracture behavior. Among the fatigue criteria, BMM predictions were the most conservative, while Morrow and SWT showed closer agreement at higher stress levels. This research highlights the effectiveness of advanced modeling and material characterization techniques in improving the accuracy of fatigue life predictions for spot welds in the automotive industry.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".