Effect of Thermal and Stress Fields on the Microstructure of a Thin Wall Built Using Laser Solid Freeform Fabrication Process
Bibliographic record
Abstract
Temperature distribution and consequent rapid cooling determine the microstructure and final physical properties of a part fabricated using laser solid freeform fabrication (LSFF). As well, in this technique, thermal stresses are the main cause of any possible delamination and crack formation across deposited layers. In this paper, the temperature distribution and the stress field induced during the LSFF process are studied throughout the fabrication of a thin wall up to four layers. The thin wall is fabricated of stainless steel AISI 304L using a 1 kW Nd:YAG pulsed laser. Variations of the microstructure and geometry of the wall are studied. A 3D dynamic numerical model of the multilayer LSFF process is used to interpret the experimental results in terms of the temperature distribution, stress field and microstructure. The experimental results show that the stress concentrations at the end points of the wall, which are due to the higher temperature gradient at these regions, are the locations for possible delaminations and crack formations. Different types of microstructures are observed at the various locations within the same layer due to the different cooling rate. While numerical results confirm the experimental findings, they also show that it is possible to reduce the maximum stress by preheating the substrate.
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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 | 0.000 |
| 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) | 0.001 | 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".