Meshfree numerical simulation of hybrid friction stir welding with adhesive bonding
Bibliographic record
Abstract
As interest for lightweight vehicles continues to increase, there has been significant progress in the development of hybrid joining approaches. A process that has been gaining interest is friction stir welding combined with adhesive bonding (FSW-AB). In the FSW-AB process, a lap joint is prepared with an uncured adhesive over the entire overlap region. Following the application of the adhesive, the FSW process is performed. Using this hybrid method can lead to improved joint strength as well as increased fatigue resistance. Furthermore, the adhesive acts as a sealant and protect the FSW joint from corrosion. Certainly the method is attractive; however, there are joint design aspects that must be considered such as the thermal degradation of the adhesive and the effect of the adhesive transport in the friction stir weld zone. In this work, a meshfree coupled thermo-mechanical simulation of the FSW-AB process is performed using SPHriction-3D. The set of continuum mechanics equations describing the physics of the FSW-AB process are cast into an amenable set of algebraic equations using a weak-strong form approach called smoothed particle hydrodynamics (SPH). This approach uses a Lagrangian frame of reference, making it ideal for predicting the movement and thermal history of the adhesive layer during the FSW process. Experimental results along with adhesive degradation profiles and infrared spectroscopy are compared to the simulation results. The article will focus on details of the meshfree simulation method as well as comparison between the simulation and experimental results. We will show that the numerical model is able to predict the extent and location of the adhesive degradation. Ultimately, the developed simulation approach is a powerful design tool for advanced hybrid joining methods such as the FSW-AB process.
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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".