Development of a phenomenological equation to predict tool wear in friction stir welding by steel grades in Al/Steel lap joining
Bibliographic record
Abstract
Since its introduction in the industry, the amount of structures being joined by friction stir welding (FSW) are in progression. Even if low-melting point materials such as aluminium or magnesium represents the highest number of applications, this method can also successfully join materials with different melting point as in the case of aluminium to steel. As lightweigthing structures gained ground in the last few years and will still continue to grow, joining processes combining aluminium to steel effectively need to be developed further. When it comes down to join aluminium to steel, the most common configuration is lap joint which the FSW pin enters into the bottom sheet substrate, thus the steel material. In order to have a FSW tool that lasts a sufficient amount of time, the material needs to sustain high stresses and temperatures and still be affordable, such as WC-based tools. The wear rate of the tool used during the process needs to be known and understood as it dictates the applicability of the FSW method depending of the application. As an example, the sub-frame assembly of the Honda Accord 2013 is joined using aluminium to steel FSW lap joining. Be able to predict when the tool reaches a point where it is no longer effective to produce a sound weld can certainly help to evaluate tool life. Actually, a few papers have produced a series of equations or processes that can predict material losses but they are constraints at the application designed for and will be presented further. This study will assess FSW tool life prediction upon different material configurations, mainly steel grades as it impacts greatly the wear behavior of the tool.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".