Effect of Preheating on the Delamination and Crack Formation in Laser Solid Freeform Fabrication Process
Bibliographic record
Abstract
This paper presents a numerical-experimental investigation on the effects of preheating the substrate on the potential delamination and crack formation across the parts fabricated using the Laser Solid Freeform Fabrication (LSFF) process. For this purpose, the temperature distributions and stress fields induced during the multilayer LSFF process, and their correlation with the delamination and crack formation are studied throughout the numerical analysis and the experimental fabrication of a four-layer thin wall of SS304L. A 3D time-dependent numerical approach is used to simulate the LSFF process, and also interpret the experimental results in terms of the temperature distribution and the thermal stress fields. The numerical results show that by preheating the substrate prior to the fabrication process, the thermal stresses throughout the process domain substantially reduce. Accordingly, this can result in the reduction of potential micro-cracks formation across the fabricated part. Preheating also decreases the transient time for the development of a proper melt pool which is an important factor to prevent poor bonding between deposited layers. The experimental results are used to verify the numerical findings as well as the feasibility of preheating on the reduction of the micro-cracks formed throughout the fabrication process.
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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 | 0.000 |
| 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".