High-cycle fatigue behavior and chemical composition empirical relationship of low carbon three-sheet spot-welded joint: An application in automotive industry
Bibliographic record
Abstract
In this paper, the authors have attempted to provide an empirical relationship between the fatigue behavior of three-sheet spot-welded joint and the chemical composition of low carbon steel. To this end, the application of this joint in the automotive industry was considered and laboratory samples were prepared based on the actual specifications in the industry, including the raw material (i.e., material and thickness of the primary sheets), the resistance spot welding (RSW) process parameters, and other factors. The results of tensile and quantometric tests along with microscopic observations were utilized to evaluate the raw material and to study the compliance of the steel grade with the required standards. Next, axial cyclic test was performed in order to extract the high-cycle fatigue (HCF) properties of three-sheet spot-welded joint. Finally, a relationship between the number of cycles to failure of the spot-welded joint, repetitive load level, and the percentage of constituent elements was presented by multiple linear regression (MLR) technique. The results showed that the greatest effect of the constituent elements is when we are in the regime of low-cycle fatigue (LCF) and by moving towards the HCF regime, its importance decreases until it becomes almost ineffective in the very-high-cycle fatigue (VHCF) area. In addition, the presented relationship is able to predict the fatigue behavior of three-sheet spot-welded joint via the chemical composition of the primary sheet and the cyclic force with a maximum error of 13.8% compared to the experimental results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".