Toward Stabilizing the Keyhole in Laser Spot Welding of Aluminum: Numerical Analysis
Bibliographic record
Abstract
The inherent instability of laser welding, particularly keyhole instability, poses significant challenges in industrial applications, leading to defects such as porosities that compromise weld quality. Various forces act on the keyhole and molten pool during laser welding, influencing process stability. These forces are categorized into those promoting keyhole opening and penetration (e.g., recoil pressure) and those promoting keyhole collapse (e.g., surface tension, Darcy's damping forces), increasing instability and defect likelihood. This paper provides a comprehensive instability analysis to uncover key factors affecting keyhole and process instability, presenting future avenues for improving laser welding stability. Using a novel numerical method for simulating laser spot welding on aluminum with COMSOL Multiphysics 5.6, we investigated the effect of laser pulse shaping on keyhole and process instability. Our analysis focused on keyhole morphology, fluid flow behaviour, and force analysis. The results indicated that the curvature effect, Marangoni effect, and Darcy's damping force are primary contributors to instability, with the curvature effect and Darcy's damping force being the most dominant. Additionally, erratic and high-velocity magnitudes induce intense fluid flow behaviour, exacerbating keyhole instability. Moreover, single/quadruple peak triangular and variant rectangular ramp-down pulse shapes produced the least instability, while multi-pulse rectangular shapes exhibited intense instability. It was found that combining triangular/rectangular pulse shapes can reduce force and keyhole instability by smoothing spontaneous force spikes, resulting in a more stabilized welding process. Controlling fluid flow and abrupt force changes with appropriate pulse shaping is key to defect-free welded products.
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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 | 0.001 |
| 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".