Investigation of the Mechanical Characteristics of Metal 3D Printing at Different Build Orientation and Directions
Bibliographic record
Abstract
Abstract In this paper, metal 3D printing technology was used to investigate the effects of build orientation angles and direction on the mechanical characteristics of 316L stainless steel produced by the Bound Metal Deposition (BMD) process. Several orientation angles (0 deg, 45 deg, and 90 deg) of the test specimens were made for both horizontal and vertical build directions. The tensile testing apparatus was used on metal 3D-printed samples to determine their mechanical characteristics, such as Young's modulus, elongation at break, and ultimate strength. Brinell hardness tester was also used to compare the hardness of 3D-printed samples to the original native material. In addition, the porosity and microstructure of the printed samples were analyzed in this study. The findings of this investigation revealed how, in comparison to the original material, the mechanical properties of the metal 3D-printed material rely on the raster orientation and build type. The experimental results demonstrate that the 3D-printed parts using the BMD process have comparable ultimate tensile strength and elongation at break compared to the original material for 0 deg orientation and horizontal build direction. The elongation at break was found to depend strongly on the build direction for 45 deg and 90 deg printing orientation. Further, Young's modulus was found to be much lower than the original material using the BMD 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".