Optimization of Friction Stir Weld Joint Quality Using a Meshfree Fully-Coupled Thermo-Mechanics Approach
Bibliographic record
Abstract
There is currently a need for an efficient numerical optimization strategy for the quality of friction stir welded (FSW) joints. However, due to the computational complexity of the multi-physics problem, process parameter optimization has been a goal that is out of reach of the current state-of-the-art simulation codes. In this work, we describe an advanced meshfree computational framework that can be used to determine numerically optimized process parameters while minimizing defects in the friction stir weld zone. The simulation code, SPHriction-3D, uses an innovative parallelization strategy on the graphics processing unit (GPU). This approach allows determination of optimal parameters faster than is possible with costly laboratory testing. The meshfree strategy is firstly outlined. Then, a novel metric is proposed that automatically evaluates the presence and severity of defects in the weld zone. Next, the code is validated against a set of experimental results for ½” AA6061-T6 butt joint FSW joints. Finally, the code is used to determine the optimal advancing speed and rpm while minimizing defect volume based on the proposed defect metric.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".