Applicability of Laser Polishing on Inconel 738 Surfaces Fabricated Through Direct Laser Deposition
Bibliographic record
Abstract
Today's manufacturing industry requires novel technologies capable to improve process versatility, rapidity as well as the surface quality of the parts fabricated through additive manufacturing.A cost/process-effective manufacturing solution capable to meet these requirements is represented by the direct laser deposition (DLD) technology.DLD is essentially an additive manufacturing (AM) process that can accurately fabricate complex freeform geometries.The main drawback of DLD is constituted by the reduced surface quality that is in fact an unavoidable characteristic of the AM processes.It was found that the best areal surface roughness (Sa) occurs on the front wall characterized by a +90° angle (or clockwise rotation) between DLD feed and flow vectors.More specifically, while the front wall is characterize by Sa = 0.704µm, the rear/back wall (-90° or counterclockwise rotation) is characterized by Sa = 3.861µm because powder is distributed and affixed in an already solidifying molten pool.To counteract this DLD process inconsistency, high-speed laser polishing (LP) can be used as a post processing technique capable to significantly improve the post-DLD surface quality.Along these lines, LP can eliminate and/or reduce the time and the cost of post-DLD surface finishing operations.Preliminary experimental results demonstrate that LP improves the quality of DLDgenerated surfaces by decreasing with up to 70% the surface roughness (Sa LP(90deg) = 0.211 µm, Sa LP(-90deg) = 0.444 µm) through a redistribution of melted micro-peaks into micro-valleys.The combination of these two laser-based technologies offers an economic, ergonomic, and ecologic fabrication option and opens up avenues for future implementations of computer-based adaptive control, self-optimization, and online monitoring techniques.
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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 | 0.000 | 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) | 0.002 | 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".