Computational modelling of SLM additive manufacturing of metals
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".