Effects of laser welding parameters on magnetic materials with the aid of a mechatronic measuring system
Bibliographic record
Abstract
This article reports results from an experimental investigation into laser welding of ferromagnetic material, which, for example, is of interest to the European Space Agency. The effects of laser welding power and translation velocities on the magnetism of ferromagnetic materials were studied. An automated system using a Hall effect transducer was constructed to measure the magnetic field transverse to the laser weld before welding, immediately after welding and three days after welding. The results show that immediately after welding the magnetization of the melt pool and heat affected zone are decreased, with the greatest decrease occurring in the weld. Interestingly, after a period of three days, during which the samples were isolated, the magnetization in the area adjacent to the weld had recovered. Specimens of iron magnet were used for the experiments; Ferranti’s MFKP 1.2 kW CO2 and GSI Lumonics Nd3+ yttrium–aluminum–garnet (YAG) lasers were utilized to weld the specimens. From both Nd3+ YAG and CO2 laser welding results, it can be concluded that at high workpiece translation speeds an optimized laser power should be used in order to minimize the size of the demagnetized zone. We also found that the specimens welded with the Nd3+ YAG pulsed laser show a better remagnetization rate and recovery than the specimens welded with the continuous wave CO2 laser.
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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 | 0.002 |
| 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) | 0.001 | 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".