A new approach to laser DED as a repair technology with laser mesh deposition
Bibliographic record
Abstract
Controlling the heat input and exploring alternate feedstocks for DED is necessary to improve the versatility of DED as a repair technique. In this research, a new DED method using metallic mesh feedstocks and laser beam oscillations. This method is shown to have good stability, repeatability and good bonding with the substrate comparable to other DED processes. The best depositions show a dilution above 40% and a height of more than 1.2 mm. The deposits show similar results to wire DED and powder DED with low dilution levels producing dense clads with no large pores and some grain refinements at the edges of the tracks. This then manifests as a potentially viable repair process with the advantage of a more flexible material input facilitating the delivering of the feedstock material for hard to access repair operations. Furthermore, this process has shown its capability to be used with various energy profiles and mesh density paving the way for a better control of the microstructure for complex repair operations. This new DED alternative method has shown its capability to run efficiently with various scan strategies and feedstocks. Hence, laser mesh deposition could be used to deposit material on inhomogeneous surfaces by using carefully designed scanning strategies and mesh density during the process increasing the repair capabilities of DED.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".