Optimization of laser welded ASTM A36 mild steel with different laser beam oscillation patterns utilizing experimental and simulation data
Bibliographic record
Abstract
Recently, there has been an increase in the use of laser beam welding of mild steel in various industries, including petroleum refineries, power plants, pharmaceuticals, and even residential areas. This research paper focuses on studying the effects of laser welding process parameters, such as laser power and welding speed, on the tensile strength of welds. To do this, three types of laser beam oscillations (sinusoidal path, triangular path, and square path) were performed to weld 125mm x 60 and 1.8 thick sheets of ASTM A36 mild steel alloy. The researchers used statistical tools such as ANOVA to generate mathematical models and experimental designs using the Taguchi method. The results indicate that the optimal welded joint has good mechanical properties after laser welding. For ASTM A36 mild steel, the optimal parameters for laser welding are a laser power of 1800 W, a welding speed of 50 mm/s, and triangular welding mode.
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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 | 0.000 |
| 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".