A Comparison Between Hardness-Scaling and Ball-Indentation Techniques on Predicting Stress/Strain Distribution and Failure Behavior of Resistance Spot Welded Advanced High Strength Steel
Bibliographic record
Abstract
Abstract To accurately model the resistance spot welding (RSW) joint in finite element analysis (FEA), the constitutive behavior of materials in various weld regions such as heat-affected zone (HAZ) should be measured. Due to the sharp temperature gradient through RSW specimens, microstructural and corresponding mechanical properties of weld regions are different. Additionally, the size of RSW is small; hence, it is challenging to directly measure the stress–strain curve of materials. In this regard, hardness-scaling and ball-indentation techniques are among the popular methods to in-directly measure the stress–strain curve of these materials. However, the effectiveness of these two techniques on predicting the stress/strain distribution and failure behavior of resistance spot welded advanced high strength steels (AHSS) is not clear. In the present work, the stress–strain curves obtained through hardness-scaling and ball-indentation techniques have been compared. The stress/strain distribution and failure behavior of the resistance spot welded AHSS specimen have been simulated by the stress–strain data obtained using the two methods. The simulation results have been compared with experimental analysis. The results showed that both methods can accurately predict the failure location. With the comparison of FEA results with experiment analysis, it was shown that the ball-indentation method provides slightly better predictions of failure behavior compared to the hardness-scaling method. However, the harness scaling method is a simple and convenient technique, which can be implemented as a qualitative analysis for the failure behavior of RSW joints.
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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 | 0.001 | 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".