Experimental Investigation of Laser Welding Process in Butt-Joint Configurations
Bibliographic record
Abstract
This paper presents an experimental investigation of laser welding low carbon galvanized steel in butt-joint configurations. The experimental work is focused on the effects of various laser welding parameters on the welds quality. The investigations are based on a structured experimental design using the Taguchi method. Welding experiments are conducted using a 3 kW Nd:YAG laser source. The selected laser welding parameters (laser power, welding speed, laser fiber diameter, gap between sheets and sheet thickness) are combined and used to evaluate the variation of four weld quality attributes (bead width, penetration depth, underfill and hardness) and to identify the possible relationship between welding parameters and weld physical and geometrical attributes. The effects of these parameters are studied using ANOVA to find their contributions to the variation of different weld characteristics. Plots of the main effects and the interaction effects are also used to understand the influence of the welding parameters. The results reveal that all welding parameters are relevant to bead width (BDW) and depth of penetration (DOP) with a relative predominance of laser power and welding speed. The effect of laser fiber diameter on penetration depth is insignificant. Typical gap-dependent weld shapes show that a small gap results in a narrower and deeper weld. Due to the standard sheared edge, an underfill between 5% and 10% occurs for no-gap experiments. The resulting hardness values are relatively similar for all the experimental tests.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".