Computational modelling of SLM additive manufacturing of metals
Why this work is in the frame
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Bibliographic record
Abstract
Additive manufacturing (AM) is a technology that can create 3D structures by depositing or melting material in a layer-by-layer manner. This paper focuses on the metal-based powder bed fusion AM approach, specifically the selective laser melting (SLM) technique. The repetitive hot and cold cycles associated with AM, causes localised compression and tension giving rise to significant residual stresses, which can lead to shape loss, structural failure, etc. Numerous parameters determine the thermal gradient; these include the thermal characteristics of the powder, the bed temperature, and the part size. This investigation describes the associated problem formulation and numerical resolution in the SLM simulation. An ANSYS-additive model is developed to determine the parameter dependence on the process. An efficient parameter calibration algorithm is proposed to generate an accurate numerical model. Three numerical studies are conducted using a vertical prism, a horizontal prism, and an L-shaped structure also compared with the experimental data. [Submitted 25 July 2020; Accepted 10 December 2020]
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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 | 0.001 | 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 | 0.000 | 0.000 |
| 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 it