A Finite Element Analysis of the Effects of Preheating Substrate Temperature and Power Input on Selective Laser Melting
Bibliographic record
Abstract
Several parameters are defined before the Selective Laser Melting printing process, which may depend on the manufacturer of the equipment, but in general, we commonly encounter hatch distance, scanning speed, layer thickness, laser power input, scanning strategy, overlap distance, and substrate preheating temperature as the parameters that mainly define the printing process. The last parameter is the focus of this study, which is applied to a finite element model to simulate temperature distributions over one layer thickness of the powder bed. The substrate temperature and power input affect the cooling rates and temperature gradients imposed on the powder bed, consequently influencing the component’s final property, surface finishing, and accuracy (dimensioning tolerances). The current FEM model showed that the preheat substrate temperature played different roles depending on which power input is used; however, there is an observed trend that is the reduction in temperature gradients in the powder bed overall when higher substrate temperatures are used.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".