Influence of Geometric Parameters and Their Variability on Fatigue Resistance of Spot-Weld Joints
Bibliographic record
Abstract
<div class="htmlview paragraph">Spot welding is the primary method of joining sheet metal for body and structural applications in the ground vehicle industry. A typical automobile may contain over 5000 spot welds. The fatigue failure of spot welded joints results in the degradation of both structural and Noise, Vibration, and Harshness (NVH) performance. Therefore, designers need reliable information about the total fatigue life of spot welded joints early on in the design phase.</div> <div class="htmlview paragraph">Currently, automotive structures are employing ever increasing amounts of Advanced High Strength Steels (AHSS) including dual-phase steels. As a result, automotive designers require fatigue strength information on AHSS spot welds. The Auto/Steel Partnership (A/SP) has conducted fatigue tests with lap shear and coach peel specimens made of AHSS, HSLA and low carbon steels. Overall, the test data showed good correlation with the applied load range for all of the materials, regardless of base material strength. However, some scatter was observed in fatigue test data, especially in the data obtained from coach peel testing. This scatter may have resulted from several sources.</div> <div class="htmlview paragraph">In order to understand the sources of scatter, the effect of geometric variations, material microstructure, and mechanical properties on fatigue life is investigated. Specifically, the variability in nominal values of spot weld diameter, specimen geometry including specimen thickness, width, etc., and hardness values obtained in the HAZ and fusion zone are examined. The results of this study shed light on the importance of geometric parameters, such as specimen thickness and weld nugget diameter, and their large impact on the fatigue strength variability.</div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".