An effective statistical approach to determine the most contributing process parameters on the surface roughness quality of hastelloy X components made by laser powder-bed fusion
Bibliographic record
Abstract
One of the main challenges in laser powder-bed fusion (LPBF) of metal powders is to determine the correlation between the key process parameters and physical properties of the produced components. Additionally, unlike conventional manufacturing processes, LPBF, as one of the many additive manufacturing processes, involves a large number of process parameters hindering the use of conventional factorial design approaches for optimization. In this paper, a modified Plackett-Burman method supported by an experimental analysis is proposed to identify the most significant parameters on the surface roughness quality of the LPBF-made Hastelloy X coupons using an EOS 290 machine. Among more than 100 process parameters, 23 parameters are chosen for this study including laser power and speed for core, skin and contour scans as well as layer thickness, geometry, orientation, location, etc. The surface roughness values (Sq) are measured using a confocal microscope (VK-X250, Keyence, Japan). Then, the roughness measurements are statistically analyzed in Minitab® to determine the most significant process parameters affecting the roughness of the LPBF printed samples. Results of the statistical approach show that the skin thickness, geometry, and layer thickness are the most significant parameters affecting the surface roughness of Hastelloy X components made by the LPBF process.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".