Influence of ultrasonic excitation on the mechanical characteristics of SLM 304L stainless steel
Bibliographic record
Abstract
Selective laser melting (SLM) is a layer by layer powder bed additive manufacturing process that can be used to create high complexity parts. Stainless steel 304L is a material which can be used in a wide range of applications including medical, aerospace, and automotive industries due to its high corrosion resistance. These same industries are also the first users of SLM and research into the combined use of the two can provide benefits for both. Ultrasonic excitation in casting has shown to decrease grain size due to the effect of the oscillating pressure waves on the nucleation conditions. While ultrasonic excitation has been used the solidifying of metals made through casting, it has not been used before in the SLM process. An investigation on the effect of ultrasonic excitation was conducted on the mechanical properties of the material. The characteristics compared were nanohardness, Young’s Modulus, and the anisotropic behavior of said properties. Given that the nucleation conditions were altered, the microstructure was affected by the pressure waves and offset columnar grains development which are formed due to the axial heatsink into the substrate, and thus ensuring a more homogenous development of grains. Given enough power, ultrasonic vibrations should ensure a more even distribution of grains and a reduction of columnar grains. These in term resulted in a reduction of anisotropic behavior, and an increase in nanohardness. The study compared samples made both with and without the ultrasonic excitation applied, as well as with different orientations for anisotropy analysis. A potential improvement in mechanical properties under the influence of ultrasonic vibrations could lead to wider acceptance of the SLM process. Further improvements, such as eliminating the need for thermal post processing could further increase the usability of the technology and 304L as a cheap material for SLM.
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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) | 0.002 | 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".