Multi-Response Optimization of Aluminum Laser Spot Welding with Sinusoidal and Cosinusoidal Power Profiles Based on Taguchi–Grey Relational Analysis
Bibliographic record
Abstract
Laser weld quality remains a critical priority across nearly all industries. However, identifying optimal laser parameter sets continues to be highly challenging, often relying on costly, time-consuming trial-and-error experiments. This difficulty is largely attributed to the severe fluctuations and instabilities inherent in laser welding, particularly keyhole instabilities. This study examines the impact of laser power modulation parameters, which, when properly applied, have been found effective in controlling and minimizing process instabilities. The investigated parameters include different pulse shapes (sinusoidal and cosinusoidal) and their associated characteristics, namely frequency (100-800 Hz) and amplitude (1000-4000 W). The impact of these modulation parameters on keyhole mode laser spot welding performance in aluminum is investigated. Using a Taguchi experimental design, a series of tests were developed, focusing on eight key welding responses, including keyhole dimensions, mean temperature, and the variability of instability-inducing forces and related factors affecting process stability. Grey relational analysis (GRA) combined with analysis of variance (ANOVA) is applied to identify the optimal combinations of laser parameters. The results indicate that low amplitude (1000 W), low to intermediate frequencies (100-400 Hz), and cosinusoidal waveforms significantly enhance weld quality by improving process stability and balancing penetration depth. Among the factors, amplitude has the greatest impact, accounting for over 50% of the performance variation, followed by frequency and pulse shape. The findings provide clear guidance for optimizing laser welding parameters to achieve stable, high-quality aluminum welds.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".