Experimental Investigation of Laser Welding Process in Overlap Joint Configuration
Bibliographic record
Abstract
This paper presents an experimental investigation of laser overlap welding of low carbon galvanized steel. Based on a structured experimental design using the Taguchi method, the investigation is focused on the evaluation of various laser welding parameters effects on the welds quality. 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 sheets thickness) are combined and used to evaluate the variation of three geometrical characteristics of the weld (penetration depth, bead width at the surface and bead width at the interface). Various improved statistical tools are used to analyze the effects of welding parameters on the variation of the weld quality and to identify the possible relationship between these parameters and the geometrical characteristics of the weld. The results reveal that the reached hardness values are similar for all the experimental tests and all welding parameters are relevant to the weld quality with a relative predominance of laser power and welding speed. The effect of the gap is relatively limited. The investigation results reveal also that there are many options to consider for building an efficient welds quality prediction model. Results achieved using an artificial neural network based simplified model provide an indication of the prediction model performances.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".