The influences of dynamic behavior characteristics of molten pool and keyhole on pore formation in oscillating laser beam welding of hidden T-joint with gap
Bibliographic record
Abstract
The pore formation process is significantly influenced by the dynamic behaviors of molten pool and keyhole. To analyze the conventional laser beam welding (CLBW) and oscillating laser beam welding (OLBW) of hidden T-joint with a gap, a 3D dynamic model of molten pool and keyhole is proposed by considering an improved ray tracing model based on virtual grid refinement and a weighted average method for recoil pressure of dissimilar materials. The established model is validated by comparing the experimental result with simulated result. The dynamic behaviors of molten pool and keyhole in CLBW and OLBW are calculated through solving the model. The evolution processes of gas cavities during CLBW and OLBW are analyzed and discussed in details. The results demonstrate that the formation of gas cavity connected to the joint gap is due to the keyhole collapse. Additionally, the flow of molten metal (MM) from the face plate toward the joint gap causes the depression of upper surface of molten pool. During the collapse of the gas cavity, the narrow gas cavity channel is separated into two parts by the MM and the rear of gas cavity is evolving into the pore defect at the joint gap gradually. Compared to CLBW, the probability of pore defect occurring at the joint gap during OLBW is decreased, which indicates that OLBW is beneficial for reducing the pore defect in weld. The developed model is helpful for understanding the influences of dynamic behaviors on pore formation during CLBW and OLBW of hidden T-joint with a gap.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".