Evaluating Bead Geometry, Hardness, and Residual Stress for a Multi-Layer 316L Laser-Wire Based DED Block
Bibliographic record
Abstract
Abstract Additive Manufacturing (AM) has revolutionized manufacturing by enabling the layer-by-layer fabrication of components from 3D model data, offering unprecedented design flexibility. Despite its numerous advantages, concerns persist regarding costs, technology readiness, and product reliability. AM excels in low volume production runs and processes like Directed Energy Deposition (DED) have gained recognition for manufacturing large metal parts and component repair. However, understanding the influence of heating and cooling cycles on final part properties remains a crucial research gap. This study focuses on investigating the impact of geometric location for a multi-layer sample with a one-way deposition sequence on the microstructure, bead geometry, and mechanical properties (Vickers microhardness and residual stress) of 316L DED-manufactured parts. By examining these characteristics across different geometric locations and comparing the experimental data to simulation results, this study aims to provide valuable insights to build a foundation for optimizing the DED manufacturing 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 | 0.000 |
| 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".