A coupled time-dependent numerical simulation on temperature and stress fields in laser solid freeform fabrication process
Bibliographic record
Abstract
This paper presents a coupled 3D time-dependent numerical approach for modeling the laser solid freeform fabrication (LSFF) process by which the geometry of the deposited materials, temperature distribution, and stress field can be predicted throughout the process. In the proposed method, coupled thermal and stress distributions are numerically obtained assuming the interaction between the laser beam and the powder stream is decoupled. Main process parameters affected by a multilayer deposition due to the formation of non-planar surfaces such as powder catchment efficiency are incorporated into the modeling. Fabrication of a four-layer thin wall of AISI 304L steel is modeled using the proposed algorithm. The geometry of the wall, the temperature, and the stress fields across the modeling domain are studied throughout the fabrication process. The model is then used to investigate the effects of preheating, and clamping the substrate to the workstation. Results show that preheating improves the process by reducing the thermal stresses as well as the settling time for the formation of a steady-state melt pool in the first layer. The results also indicate that clamping the substrate decreases thermal stresses at its critical locations (i.e. deposition region). The reliability and the accuracy of the model are experimentally verified.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".