Effects of Process Parameters on Surface Finish in Laser Solid Freeform Fabrication
Bibliographic record
Abstract
Laser Solid Freeform Fabrication (LSFF) is a flexible rapid prototyping technique in which a laser beam is used to melt and deposit the injected powder in a layer-by-layer fashion to form 3D components. In this paper, the effects of the main process parameters such as laser power and traverse speed on the surface finish of the parts fabricated using the LSFF process are investigated. Since these process parameters and their variations determine the microstructure and other resultant physical qualities of the fabricated parts, they should carefully be selected to increase the surface quality without compromising other quality aspects of the outcomes. For this purpose, along with the experimental analyses, an experimentally verified 3D time-dependent numerical model is employed to comprehensively study the temperature distributions, thermal stress fields, and their variations resulted from different process parameters and consequently different surface finishes. The experimental investigations are conducted through the fabrications of several thin walls of AISI 303L stainless steel using a fiber laser with a maximum power of 1100 W. The numerical and experimental results show under a constant power feed rate by increasing the process speed while optimizing the laser power, the surface finish of the fabricated parts can improve without compromising the melt pool conditions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".