Effect Of Printing Parameters On The High Strain Rate Compressive Behaviour Of Additively Manufactured 316L Stainless Steel Alloy
Bibliographic record
Abstract
316L stainless steel alloy is widely used in hostile environments structural components due to attractive mechanical properties including good corrosion resistance and exceptional strength at high temperatures. The emergence of 3D-printing provides flexibility of 316L stainless steel alloy parts for various structural applications. A decent mix of metal powders, a combination of special printing parameters and printing orientation are however needed to improve the properties of the 3D-printed parts. Therefore, in the current studies, the effect of printing parameters and the build orientation on the microstructure and high strain rate properties of 3D-printed 316L stainless steel alloy was investigated. Printing parameters such as hatch spacing, laser power, scan speed as well as build direction have effect on the high strain rate compressive properties and microstructure of 316L stainless steel alloy. The microstructure will be characterized utilizing Optical Microscopy (OM) and Scanning Electron Microscopy (SEM) for final printed samples. Direct Impact Hopkinson Pressure Bar (DIHPB) will be used to examine the high strain rate deformation and failure modes. The microstructure of the samples was further characterized with Optical Microscopy (OM) and Scanning Electron Microscopy (SEM) post impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".